Use GH Archive to pull 2024 stats

.gitignore

@@ -8,5 +8,6 @@ static/data
 .vscode/settings.json
 .env
 .evidence/meta
+.meltano/
 *.pyc
-__pycache__
+__pycache__

meltano.yml

@@ -0,0 +1,40 @@
+version: 1
+default_environment: dev
+project_id: 64e752dd-4728-4bbd-b8d0-9af37a481f6b
+environments:
+- name: dev
+- name: staging
+- name: prod
+send_anonymous_usage_stats: false
+plugins:
+  extractors:
+  - name: tap-gharchive
+    namespace: tap_gharchive
+    pip_url: -e .
+    executable: tap-gharchive
+    config:
+      api_url: https://data.gharchive.org
+      streams:
+      - name: github_events
+        path: /2023-10-01-0.json.gz
+        primary_keys:
+          - id
+        schema:
+          properties:
+            type:
+              type: string
+            actor:
+              type: object
+            repo:
+              type: object
+            created_at:
+              type: string
+            org:
+              type: object
+            payload:
+              type: object
+      start_date: '2023-10-01'
+  loaders:
+  - name: target-jsonl
+    variant: andyh1203
+    pip_url: target-jsonl

pipeline/gharchive_pipeline.py

@@ -0,0 +1,218 @@
+import dlt
+from dlt.sources.sql_database import sql_table
+import pendulum
+from typing import Iterator, Dict, List
+from collections import defaultdict
+import os
+
+
+def process_events_for_stats(events: List[Dict]) -> Dict:
+    """Process a batch of events to calculate statistics."""
+    stats = {
+        "repo_metrics": defaultdict(
+            lambda: {
+                "stars": 0,
+                "forks": 0,
+                "watchers": 0,
+            }
+        ),
+        "traffic": defaultdict(
+            lambda: {
+                "views": defaultdict(int),
+                "clones": defaultdict(int),
+                "unique_views": defaultdict(int),
+                "unique_clones": defaultdict(int),
+            }
+        ),
+        "contributors": defaultdict(
+            lambda: {
+                "commits": 0,
+                "additions": 0,
+                "deletions": 0,
+                "first_contribution": None,
+            }
+        ),
+        "issues": {
+            "total": 0,
+            "open": 0,
+            "closed": 0,
+            "response_times": [],
+            "close_times": [],
+            "daily_opened": defaultdict(int),
+            "daily_closed": defaultdict(int),
+        },
+        "pull_requests": {
+            "total": 0,
+            "open": 0,
+            "closed": 0,
+            "response_times": [],
+            "close_times": [],
+            "daily_opened": defaultdict(int),
+            "daily_closed": defaultdict(int),
+        },
+    }
+
+    for event in events:
+        repo_name = event.get("repo", {}).get("name", "").split("/")[-1]
+        event_type = event.get("type")
+
+        # Process different event types
+        if event_type == "WatchEvent":
+            stats["repo_metrics"][repo_name]["stars"] += 1
+        elif event_type == "ForkEvent":
+            stats["repo_metrics"][repo_name]["forks"] += 1
+        elif event_type == "PushEvent":
+            author = event.get("actor", {}).get("login")
+            if author:
+                stats["contributors"][author]["commits"] += len(
+                    event.get("payload", {}).get("commits", [])
+                )
+                for commit in event.get("payload", {}).get("commits", []):
+                    stats["contributors"][author]["additions"] += commit.get(
+                        "stats", {}
+                    ).get("additions", 0)
+                    stats["contributors"][author]["deletions"] += commit.get(
+                        "stats", {}
+                    ).get("deletions", 0)
+        elif event_type == "IssuesEvent":
+            action = event.get("payload", {}).get("action")
+            issue = event.get("payload", {}).get("issue", {})
+            is_pr = "pull_request" in issue
+
+            stats_key = "pull_requests" if is_pr else "issues"
+            stats[stats_key]["total"] += 1
+
+            if action == "opened":
+                stats[stats_key]["open"] += 1
+                created_date = pendulum.parse(issue.get("created_at")).format(
+                    "YYYY-MM-DD"
+                )
+                stats[stats_key]["daily_opened"][created_date] += 1
+            elif action == "closed":
+                stats[stats_key]["closed"] += 1
+                closed_date = pendulum.parse(event.get("created_at")).format(
+                    "YYYY-MM-DD"
+                )
+                stats[stats_key]["daily_closed"][closed_date] += 1
+
+                # Calculate close time
+                if issue.get("created_at"):
+                    created_time = pendulum.parse(issue["created_at"])
+                    closed_time = pendulum.parse(event.get("created_at"))
+                    close_time = (closed_time - created_time).total_seconds()
+                    stats[stats_key]["close_times"].append(close_time)
+
+        elif event_type == "IssueCommentEvent":
+            issue = event.get("payload", {}).get("issue", {})
+            is_pr = "pull_request" in issue
+            stats_key = "pull_requests" if is_pr else "issues"
+
+            # Calculate response time for first comment
+            if issue.get("created_at") and event.get("created_at"):
+                created_time = pendulum.parse(issue["created_at"])
+                comment_time = pendulum.parse(event.get("created_at"))
+                response_time = (comment_time - created_time).total_seconds()
+                stats[stats_key]["response_times"].append(response_time)
+
+    return stats
+
+
+@dlt.source
+def gharchive_source(start_date: str = "2024-01-01", end_date: str = None):
+    """
+    Load GitHub events for nf-core organization from GH Archive BigQuery dataset.
+
+    Args:
+        start_date: Start date in YYYY-MM-DD format
+        end_date: End date in YYYY-MM-DD format (defaults to current date)
+    """
+    if end_date is None:
+        end_date = pendulum.now().format("YYYY-MM-DD")
+
+    # Get BigQuery credentials path from environment
+    # credentials_path = os.getenv('GOOGLE_APPLICATION_CREDENTIALS')
+    # if not credentials_path:
+    #     raise ValueError("GOOGLE_APPLICATION_CREDENTIALS environment variable must be set")
+
+    # Construct BigQuery connection string for ConnectorX
+    conn = f"bigquery://{credentials_path}"
+
+    @dlt.resource(write_disposition="append", name="raw_events")
+    def events(
+        start_date: str = start_date, end_date: str = end_date
+    ) -> Iterator[Dict]:
+        """Query GitHub events from BigQuery."""
+        query = f"""
+        SELECT *
+        FROM `githubarchive.day.*`
+        WHERE _TABLE_SUFFIX BETWEEN FORMAT_DATE('%Y%m%d', DATE('{start_date}'))
+                          AND FORMAT_DATE('%Y%m%d', DATE('{end_date}'))
+        AND JSON_EXTRACT_SCALAR(payload, '$.organization.login') = 'nf-core'
+        """
+
+        # Create sql_table with ConnectorX backend
+        table = sql_table(
+            conn,  # BigQuery connection string
+            query=query,
+            chunk_size=100000,  # Process in chunks for better memory usage
+            backend="connectorx",  # Use ConnectorX backend for better performance
+            reflection_level="full_with_precision",  # Get exact data types
+            backend_kwargs={"return_type": "arrow"},  # Use Arrow for better performance
+        )
+        yield from table
+
+    @dlt.resource(write_disposition="merge", primary_key="date", name="daily_stats")
+    def daily_statistics(
+        start_date: str = start_date, end_date: str = end_date
+    ) -> Iterator[Dict]:
+        """Calculate daily statistics from BigQuery events."""
+        start = pendulum.parse(start_date)
+        end = pendulum.parse(end_date)
+        current = start
+
+        while current <= end:
+            query = f"""
+            SELECT *
+            FROM `githubarchive.day.{current.format("YYYYMMDD")}`
+            WHERE JSON_EXTRACT_SCALAR(payload, '$.organization.login') = 'nf-core'
+            """
+
+            # Create sql_table with ConnectorX backend
+            table = sql_table(
+                conn,
+                query=query,
+                chunk_size=100000,
+                backend="connectorx",
+                reflection_level="full_with_precision",
+                backend_kwargs={"return_type": "arrow"},
+            )
+            daily_events = list(table)
+
+            if daily_events:
+                stats = process_events_for_stats(daily_events)
+                yield {"date": current.format("YYYY-MM-DD"), "stats": stats}
+
+            current = current.add(days=1)
+
+    return events, daily_statistics
+
+
+if __name__ == "__main__":
+    # Initialize the pipeline
+    pipeline = dlt.pipeline(
+        pipeline_name="gharchive",
+        destination="duckdb",
+        dataset_name="github_events",
+        progress="log",  # Add progress logging
+        dev_mode=True,  # Enable dev mode for better debugging
+    )
+
+    # Run the pipeline for 2024 data
+    load_info = pipeline.run(
+        gharchive_source(
+            start_date="2024-01-01", end_date=pendulum.now().format("YYYY-MM-DD")
+        ),
+        loader_file_format="parquet",  # Use parquet for better performance
+    )
+
+    print(load_info)

pipeline/pyproject.toml

@@ -12,6 +12,7 @@ dependencies = [
     "dlt[cli,duckdb,motherduck]>=1.18.2",
     "python-dotenv==1.2.1",
     "requests>=2.31.0",
+    "pendulum>=2.1.2",
     "semanticscholar>=0.11.0",
     "slack-sdk>=3.33.5",
 ]

plugins/extractors/tap-bigquery--anelendata.lock

@@ -0,0 +1,64 @@
+{
+  "plugin_type": "extractors",
+  "name": "tap-bigquery",
+  "namespace": "tap_bigquery",
+  "variant": "anelendata",
+  "label": "BigQuery",
+  "docs": "https://hub.meltano.com/extractors/tap-bigquery--anelendata",
+  "repo": "https://github.com/anelendata/tap-bigquery",
+  "pip_url": "tap-bigquery",
+  "description": "BigQuery data warehouse extractor",
+  "logo_url": "https://hub.meltano.com/assets/logos/extractors/bigquery.png",
+  "capabilities": [
+    "catalog",
+    "discover",
+    "state"
+  ],
+  "settings_group_validation": [
+    [
+      "streams",
+      "start_datetime",
+      "credentials_path"
+    ]
+  ],
+  "settings": [
+    {
+      "name": "streams",
+      "kind": "array",
+      "label": "Streams",
+      "description": "Array of objects with `name`, `table`, `columns`, `datetime_key`, and `filters` keys:\n\n- `name`: The entity name, used by most loaders as the name of the table to be created.\n- `table`: Fully qualified table name in BigQuery, with format `` `<project>.<dataset>.<table>` ``. Since backticks have special meaning in YAML, values in `meltano.yml` should be wrapped in double quotes.\n- `columns`: Array of column names to select. Using `[\"*\"]` is not recommended as it can become very expensive for a table with a large number of columns.\n- `datetime_key`: Name of datetime column to use as [replication key](https://docs.meltano.com/guide/integration#replication-key).\n- `filters`: Optional array of `WHERE` clauses to filter extracted data, e.g. `\"column='value'\"`.\n"
+    },
+    {
+      "name": "credentials_path",
+      "env": "GOOGLE_APPLICATION_CREDENTIALS",
+      "value": "$MELTANO_PROJECT_ROOT/client_secrets.json",
+      "label": "Credentials Path",
+      "description": "Fully qualified path to `client_secrets.json` for your service account.\n\nSee the [\"Activate the Google BigQuery API\" section of the repository's README](https://github.com/anelendata/tap-bigquery#step-1-activate-the-google-bigquery-api) and <https://cloud.google.com/docs/authentication/production>.\n\nBy default, this file is expected to be at the root of your project directory.\n"
+    },
+    {
+      "name": "start_datetime",
+      "kind": "date_iso8601",
+      "label": "Start Datetime",
+      "description": "Determines how much historical data will be extracted. Please be aware that the larger the time period and amount of data, the longer the initial extraction can be expected to take."
+    },
+    {
+      "name": "end_datetime",
+      "kind": "date_iso8601",
+      "label": "End Datetime",
+      "description": "Date up to when historical data will be extracted."
+    },
+    {
+      "name": "limit",
+      "kind": "integer",
+      "label": "Limit",
+      "description": "Limits the number of records returned in each stream, applied as a limit in the query."
+    },
+    {
+      "name": "start_always_inclusive",
+      "kind": "boolean",
+      "value": true,
+      "label": "Start Always Inclusive",
+      "description": "When replicating incrementally, disable to only select records whose `datetime_key` is greater than the maximum value replicated in the last run, by excluding records whose timestamps match exactly. This could cause records to be missed that were created after the last run finished, but during the same second and with the same timestamp."
+    }
+  ]
+}