Update schema with new tags: internet, owner

.github/ISSUE_TEMPLATE/community_submission.yaml

@@ -14,7 +14,7 @@ body:
         - Each object must include **at least one** of:
           - `registration_number`
           - `transponder_code_hex` (6 uppercase hex chars, e.g., `ABC123`)
-          - `openairframes_id`
+          - `planequery_airframe_id`
         - Your contributor name (entered below) will be applied to all objects.
         - `contributor_uuid` is derived from your GitHub account automatically.
         - `creation_timestamp` is created by the system (you may omit it).

.github/workflows/historical-adsb.yaml

@@ -48,7 +48,7 @@ jobs:
       matrix:
         chunk: ${{ fromJson(needs.generate-matrix.outputs.chunks) }}
       max-parallel: 3
-      fail-fast: true
+      fail-fast: false
     steps:
       - name: Checkout
         uses: actions/checkout@v4
@@ -74,12 +74,11 @@ jobs:
         env:
           START_DATE: ${{ matrix.chunk.start_date }}
           END_DATE: ${{ matrix.chunk.end_date }}
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
           python -m src.adsb.download_and_list_icaos --start-date "$START_DATE" --end-date "$END_DATE"
           ls -lah data/output/
 
-      - name: Create tar of extracted data and split into chunks
+      - name: Create tar of extracted data
         run: |
           cd data/output
           echo "=== Disk space before tar ==="
@@ -94,31 +93,16 @@ jobs:
             ls -lah extracted_data.tar
             # Verify tar integrity
             tar -tf extracted_data.tar > /dev/null && echo "Tar integrity check passed" || { echo "Tar integrity check FAILED"; exit 1; }
-            
-            # Create checksum of the FULL tar before splitting (for verification after reassembly)
-            echo "=== Creating checksum of full tar ==="
-            sha256sum extracted_data.tar > full_tar.sha256
-            cat full_tar.sha256
-            
-            # Split into 500MB chunks to avoid artifact upload issues
-            echo "=== Splitting tar into 500MB chunks ==="
-            mkdir -p tar_chunks
-            split -b 500M extracted_data.tar tar_chunks/extracted_data.tar.part_
-            rm extracted_data.tar
-            mv full_tar.sha256 tar_chunks/
-            
-            echo "=== Chunks created ==="
-            ls -lah tar_chunks/
           else
             echo "ERROR: No extracted directories found, cannot create tar"
             exit 1
           fi
 
-      - name: Upload extracted data chunks
+      - name: Upload extracted data
         uses: actions/upload-artifact@v4
         with:
           name: adsb-extracted-${{ matrix.chunk.start_date }}-${{ matrix.chunk.end_date }}
-          path: data/output/tar_chunks/
+          path: data/output/extracted_data.tar
           retention-days: 1
           compression-level: 0
           if-no-files-found: warn
@@ -156,40 +140,18 @@ jobs:
         uses: actions/download-artifact@v4
         with:
           name: adsb-extracted-${{ matrix.chunk.start_date }}-${{ matrix.chunk.end_date }}
-          path: data/output/tar_chunks/
+          path: data/output/
+        continue-on-error: true
 
-      - name: Reassemble and extract tar
+      - name: Extract tar
         id: extract
         run: |
           cd data/output
-          if [ -d tar_chunks ] && ls tar_chunks/extracted_data.tar.part_* 1>/dev/null 2>&1; then
-            echo "=== Chunk files info ==="
-            ls -lah tar_chunks/
-            
-            cd tar_chunks
-            
-            # Reassemble tar with explicit sorting
-            echo "=== Reassembling tar file ==="
-            ls -1 extracted_data.tar.part_?? | sort | while read part; do
-              echo "Appending $part..."
-              cat "$part" >> ../extracted_data.tar
-            done
-            cd ..
-            
-            echo "=== Reassembled tar file info ==="
+          if [ -f extracted_data.tar ]; then
+            echo "=== Tar file info ==="
             ls -lah extracted_data.tar
-            
-            # Verify checksum of reassembled tar matches original
-            echo "=== Verifying reassembled tar checksum ==="
-            echo "Original checksum:"
-            cat tar_chunks/full_tar.sha256
-            echo "Reassembled checksum:"
-            sha256sum extracted_data.tar
-            sha256sum -c tar_chunks/full_tar.sha256 || { echo "ERROR: Reassembled tar checksum mismatch - data corrupted during transfer"; exit 1; }
-            echo "Checksum verified - data integrity confirmed"
-            
-            rm -rf tar_chunks
-            
+            echo "=== Verifying tar integrity ==="
+            tar -tf extracted_data.tar > /dev/null || { echo "ERROR: Tar file is corrupted"; exit 1; }
             echo "=== Extracting ==="
             tar -xvf extracted_data.tar
             rm extracted_data.tar
@@ -197,7 +159,7 @@ jobs:
             echo "=== Contents of data/output ==="
             ls -lah
           else
-            echo "No tar chunks found"
+            echo "No extracted_data.tar found"
             echo "has_data=false" >> "$GITHUB_OUTPUT"
           fi
 
@@ -258,11 +220,11 @@ jobs:
           END_DATE: ${{ needs.generate-matrix.outputs.global_end }}
         run: |
           python -m src.adsb.combine_chunks_to_csv --chunks-dir data/output/adsb_chunks --start-date "$START_DATE" --end-date "$END_DATE" --skip-base --stream
-          ls -lah data/openairframes/
+          ls -lah data/planequery_aircraft/
 
       - name: Upload final artifact
         uses: actions/upload-artifact@v4
         with:
-          name: openairframes_adsb-${{ needs.generate-matrix.outputs.global_start }}-${{ needs.generate-matrix.outputs.global_end }}
-          path: data/openairframes/*.csv
+          name: planequery_aircraft_adsb-${{ needs.generate-matrix.outputs.global_start }}-${{ needs.generate-matrix.outputs.global_end }}
+          path: data/planequery_aircraft/*.csv
           retention-days: 30

.github/workflows/planequery-aircraft-daily-release.yaml

@@ -1,15 +1,10 @@
-name: OpenAirframes Daily Release
+name: planequery-aircraft Daily Release
 
 on:
   schedule:
     # 6:00pm UTC every day - runs on default branch, triggers both
     - cron: "0 06 * * *"
   workflow_dispatch:
-    inputs:
-      date:
-        description: 'Date to process (YYYY-MM-DD format, default: yesterday)'
-        required: false
-        type: string
 
 permissions:
   contents: write
@@ -27,7 +22,7 @@ jobs:
             await github.rest.actions.createWorkflowDispatch({
               owner: context.repo.owner,
               repo: context.repo.repo,
-              workflow_id: 'openairframes-daily-release.yaml',
+              workflow_id: 'planequery-aircraft-daily-release.yaml',
               ref: 'main'
             });
       
@@ -38,7 +33,7 @@ jobs:
             await github.rest.actions.createWorkflowDispatch({
               owner: context.repo.owner,
               repo: context.repo.repo,
-              workflow_id: 'openairframes-daily-release.yaml',
+              workflow_id: 'planequery-aircraft-daily-release.yaml',
               ref: 'develop'
             });
 
@@ -63,16 +58,16 @@ jobs:
 
       - name: Run FAA release script
         run: |
-          python src/create_daily_faa_release.py ${{ inputs.date && format('--date {0}', inputs.date) || '' }}
+          python src/create_daily_planequery_aircraft_release.py
           ls -lah data/faa_releasable
-          ls -lah data/openairframes
+          ls -lah data/planequery_aircraft
 
       - name: Upload FAA artifacts
         uses: actions/upload-artifact@v4
         with:
           name: faa-release
           path: |
-            data/openairframes/openairframes_faa_*.csv
+            data/planequery_aircraft/planequery_aircraft_faa_*.csv
             data/faa_releasable/ReleasableAircraft_*.zip
           retention-days: 1
 
@@ -98,10 +93,8 @@ jobs:
           pip install -r requirements.txt
 
       - name: Download and extract ADS-B data
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         run: |
-          python -m src.adsb.download_and_list_icaos ${{ inputs.date && format('--date {0}', inputs.date) || '' }}
+          python -m src.adsb.download_and_list_icaos
           ls -lah data/output/
 
       - name: Check manifest exists
@@ -171,7 +164,7 @@ jobs:
 
       - name: Process chunk ${{ matrix.chunk }}
         run: |
-          python -m src.adsb.process_icao_chunk --chunk-id ${{ matrix.chunk }} --total-chunks 4 ${{ inputs.date && format('--date {0}', inputs.date) || '' }}
+          python -m src.adsb.process_icao_chunk --chunk-id ${{ matrix.chunk }} --total-chunks 4
           mkdir -p data/output/adsb_chunks
           ls -lah data/output/adsb_chunks/ || echo "No chunks created"
 
@@ -220,14 +213,14 @@ jobs:
         run: |
           mkdir -p data/output/adsb_chunks
           ls -lah data/output/adsb_chunks/ || echo "Directory empty or does not exist"
-          python -m src.adsb.combine_chunks_to_csv --chunks-dir data/output/adsb_chunks ${{ inputs.date && format('--date {0}', inputs.date) || '' }}
-          ls -lah data/openairframes/
+          python -m src.adsb.combine_chunks_to_csv --chunks-dir data/output/adsb_chunks
+          ls -lah data/planequery_aircraft/
 
       - name: Upload ADS-B artifacts
         uses: actions/upload-artifact@v4
         with:
           name: adsb-release
-          path: data/openairframes/openairframes_adsb_*.csv
+          path: data/planequery_aircraft/planequery_aircraft_adsb_*.csv
           retention-days: 1
 
   build-community:
@@ -252,13 +245,13 @@ jobs:
       - name: Run Community release script
         run: |
           python -m src.contributions.create_daily_community_release
-          ls -lah data/openairframes
+          ls -lah data/planequery_aircraft
 
       - name: Upload Community artifacts
         uses: actions/upload-artifact@v4
         with:
           name: community-release
-          path: data/openairframes/openairframes_community_*.csv
+          path: data/planequery_aircraft/planequery_aircraft_community_*.csv
           retention-days: 1
 
   create-release:
@@ -266,13 +259,6 @@ jobs:
     needs: [build-faa, adsb-reduce, build-community]
     if: github.event_name != 'schedule'
     steps:
-      - name: Checkout for gh CLI
-        uses: actions/checkout@v4
-        with:
-          sparse-checkout: |
-            .github
-          sparse-checkout-cone-mode: false
-
       - name: Download FAA artifacts
         uses: actions/download-artifact@v4
         with:
@@ -293,8 +279,6 @@ jobs:
 
       - name: Debug artifact structure
         run: |
-          echo "=== Full artifacts tree ==="
-          find artifacts -type f 2>/dev/null || echo "No files found in artifacts"
           echo "=== FAA artifacts ==="
           find artifacts/faa -type f 2>/dev/null || echo "No files found in artifacts/faa"
           echo "=== ADS-B artifacts ==="
@@ -313,38 +297,16 @@ jobs:
           elif [ "$BRANCH_NAME" = "develop" ]; then
             BRANCH_SUFFIX="-develop"
           fi
-          TAG="openairframes-${DATE}${BRANCH_SUFFIX}"
+          TAG="planequery-aircraft-${DATE}${BRANCH_SUFFIX}"
           
-          # Find files from artifacts using find (handles nested structures)
-          CSV_FILE_FAA=$(find artifacts/faa -name "openairframes_faa_*.csv" -type f 2>/dev/null | head -1)
-          CSV_FILE_ADSB=$(find artifacts/adsb -name "openairframes_adsb_*.csv" -type f 2>/dev/null | head -1)
-          CSV_FILE_COMMUNITY=$(find artifacts/community -name "openairframes_community_*.csv" -type f 2>/dev/null | head -1)
-          ZIP_FILE=$(find artifacts/faa -name "ReleasableAircraft_*.zip" -type f 2>/dev/null | head -1)
-          
-          # Validate required files exist
-          MISSING_FILES=""
-          if [ -z "$CSV_FILE_FAA" ] || [ ! -f "$CSV_FILE_FAA" ]; then
-            MISSING_FILES="$MISSING_FILES FAA_CSV"
-          fi
-          if [ -z "$CSV_FILE_ADSB" ] || [ ! -f "$CSV_FILE_ADSB" ]; then
-            MISSING_FILES="$MISSING_FILES ADSB_CSV"
-          fi
-          if [ -z "$ZIP_FILE" ] || [ ! -f "$ZIP_FILE" ]; then
-            MISSING_FILES="$MISSING_FILES FAA_ZIP"
-          fi
-          
-          if [ -n "$MISSING_FILES" ]; then
-            echo "ERROR: Missing required release files:$MISSING_FILES"
-            echo "FAA CSV: $CSV_FILE_FAA"
-            echo "ADSB CSV: $CSV_FILE_ADSB"
-            echo "ZIP: $ZIP_FILE"
-            exit 1
-          fi
-          
-          # Get basenames for display
+          # Find files from artifacts
+          CSV_FILE_FAA=$(ls artifacts/faa/data/planequery_aircraft/planequery_aircraft_faa_*.csv | head -1)
           CSV_BASENAME_FAA=$(basename "$CSV_FILE_FAA")
+          CSV_FILE_ADSB=$(ls artifacts/adsb/planequery_aircraft_adsb_*.csv | head -1)
           CSV_BASENAME_ADSB=$(basename "$CSV_FILE_ADSB")
+          CSV_FILE_COMMUNITY=$(ls artifacts/community/planequery_aircraft_community_*.csv 2>/dev/null | head -1 || echo "")
           CSV_BASENAME_COMMUNITY=$(basename "$CSV_FILE_COMMUNITY" 2>/dev/null || echo "")
+          ZIP_FILE=$(ls artifacts/faa/data/faa_releasable/ReleasableAircraft_*.zip | head -1)
           ZIP_BASENAME=$(basename "$ZIP_FILE")
           
           echo "date=$DATE" >> "$GITHUB_OUTPUT"
@@ -357,18 +319,12 @@ jobs:
           echo "csv_basename_community=$CSV_BASENAME_COMMUNITY" >> "$GITHUB_OUTPUT"
           echo "zip_file=$ZIP_FILE" >> "$GITHUB_OUTPUT"
           echo "zip_basename=$ZIP_BASENAME" >> "$GITHUB_OUTPUT"
-          echo "name=OpenAirframes snapshot ($DATE)${BRANCH_SUFFIX}" >> "$GITHUB_OUTPUT"
-          
-          echo "Found files:"
-          echo "  FAA CSV: $CSV_FILE_FAA"
-          echo "  ADSB CSV: $CSV_FILE_ADSB"
-          echo "  Community CSV: $CSV_FILE_COMMUNITY"
-          echo "  ZIP: $ZIP_FILE"
+          echo "name=planequery-aircraft snapshot ($DATE)${BRANCH_SUFFIX}" >> "$GITHUB_OUTPUT"
 
       - name: Delete existing release if exists
         run: |
-          echo "Attempting to delete release: ${{ steps.meta.outputs.tag }}"
-          gh release delete "${{ steps.meta.outputs.tag }}" --yes --cleanup-tag || echo "No existing release to delete"
+          gh release delete "${{ steps.meta.outputs.tag }}" --yes 2>/dev/null || true
+          git push --delete origin "refs/tags/${{ steps.meta.outputs.tag }}" 2>/dev/null || true
         env:
           GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
@@ -377,7 +333,6 @@ jobs:
         with:
           tag_name: ${{ steps.meta.outputs.tag }}
           name: ${{ steps.meta.outputs.name }}
-          fail_on_unmatched_files: true
           body: |
             Automated daily snapshot generated at 06:00 UTC for ${{ steps.meta.outputs.date }}.
 

.github/workflows/process-historical-faa.yaml

@@ -1,171 +0,0 @@
-name: Process Historical FAA Data
-
-on:
-  workflow_dispatch:  # Manual trigger
-
-jobs:
-  generate-matrix:
-    runs-on: ubuntu-latest
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: Generate date ranges
-        id: set-matrix
-        run: |
-          python3 << 'EOF'
-          import json
-          from datetime import datetime, timedelta
-          
-          start = datetime(2023, 8, 16)
-          end = datetime(2026, 1, 1)
-          
-          ranges = []
-          current = start
-          
-          # Process in 4-day chunks
-          while current < end:
-            chunk_end = current + timedelta(days=4)
-            # Don't go past the end date
-            if chunk_end > end:
-              chunk_end = end
-            
-            ranges.append({
-              "since": current.strftime("%Y-%m-%d"),
-              "until": chunk_end.strftime("%Y-%m-%d")
-            })
-            
-            current = chunk_end
-          
-          print(f"::set-output name=matrix::{json.dumps(ranges)}")
-          EOF
-
-  clone-faa-repo:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Cache FAA repository
-        id: cache-faa-repo
-        uses: actions/cache@v4
-        with:
-          path: data/scrape-faa-releasable-aircraft
-          key: faa-repo-v1
-          
-      - name: Clone FAA repository
-        if: steps.cache-faa-repo.outputs.cache-hit != 'true'
-        run: |
-          mkdir -p data
-          git clone https://github.com/simonw/scrape-faa-releasable-aircraft data/scrape-faa-releasable-aircraft
-          echo "Repository cloned successfully"
-
-  process-chunk:
-    needs: [generate-matrix, clone-faa-repo]
-    runs-on: ubuntu-latest
-    strategy:
-      max-parallel: 5  # Process 5 chunks at a time
-      matrix:
-        range: ${{ fromJson(needs.generate-matrix.outputs.matrix) }}
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-      
-      - name: Restore FAA repository cache
-        uses: actions/cache/restore@v4
-        with:
-          path: data/scrape-faa-releasable-aircraft
-          key: faa-repo-v1
-          fail-on-cache-miss: true
-      
-      - name: Install dependencies
-        run: |
-          pip install -r requirements.txt
-      
-      - name: Process chunk ${{ matrix.range.since }} to ${{ matrix.range.until }}
-        run: |
-          python src/get_historical_faa.py "${{ matrix.range.since }}" "${{ matrix.range.until }}"
-      
-      - name: Upload CSV artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: csv-${{ matrix.range.since }}-to-${{ matrix.range.until }}
-          path: data/faa_releasable_historical/*.csv
-          retention-days: 1
-
-  create-release:
-    needs: process-chunk
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Download all artifacts
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-      
-      - name: Prepare release files
-        run: |
-          mkdir -p release-files
-          find artifacts -name "*.csv" -exec cp {} release-files/ \;
-          ls -lh release-files/
-      
-      - name: Create Release
-        uses: softprops/action-gh-release@v1
-        with:
-          tag_name: historical-faa-${{ github.run_number }}
-          name: Historical FAA Data Release ${{ github.run_number }}
-          body: |
-            Automated release of historical FAA aircraft data
-            Processing period: 2023-08-16 to 2026-01-01
-            Generated: ${{ github.event.repository.updated_at }}
-          files: release-files/*.csv
-          draft: false
-          prerelease: false
-
-  concatenate-and-release:
-    needs: process-chunk
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-      
-      - name: Install dependencies
-        run: |
-          pip install -r requirements.txt
-      
-      - name: Download all artifacts
-        uses: actions/download-artifact@v4
-        with:
-          path: artifacts
-      
-      - name: Prepare CSVs for concatenation
-        run: |
-          mkdir -p data/faa_releasable_historical
-          find artifacts -name "*.csv" -exec cp {} data/faa_releasable_historical/ \;
-          ls -lh data/faa_releasable_historical/
-      
-      - name: Concatenate all CSVs
-        run: |
-          python scripts/concat_csvs.py
-      
-      - name: Create Combined Release
-        uses: softprops/action-gh-release@v1
-        with:
-          tag_name: historical-faa-combined-${{ github.run_number }}
-          name: Historical FAA Data Combined Release ${{ github.run_number }}
-          body: |
-            Combined historical FAA aircraft data (all chunks concatenated)
-            Processing period: 2023-08-16 to 2026-01-01
-            Generated: ${{ github.event.repository.updated_at }}
-          files: data/openairframes/*.csv
-          draft: false
-          prerelease: false

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2026 OpenAirframes
+Copyright (c) 2026 PlaneQuery
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

community/2026-02-12/ggman12_2026-02-12_87250f4c.json

@@ -0,0 +1,19 @@
+[
+  {
+    "contributor_uuid": "2981c3ee-8712-5f96-84bf-732eda515a3f",
+    "creation_timestamp": "2026-02-12T20:52:47.207684+00:00",
+    "registration_number": "N12345",
+    "tags": {
+      "internet": "starlink"
+    }
+  },
+  {
+    "contributor_uuid": "2981c3ee-8712-5f96-84bf-732eda515a3f",
+    "creation_timestamp": "2026-02-12T20:52:47.207684+00:00",
+    "tags": {
+      "internet": "viasat",
+      "owner": "John Doe"
+    },
+    "transponder_code_hex": "ABC123"
+  }
+]

infra/app.py

@@ -0,0 +1,11 @@
+#!/usr/bin/env python3
+import os
+import aws_cdk as cdk
+from stack import AdsbProcessingStack
+
+app = cdk.App()
+AdsbProcessingStack(app, "AdsbProcessingStack", env=cdk.Environment(
+    account=os.environ["CDK_DEFAULT_ACCOUNT"],
+    region=os.environ["CDK_DEFAULT_REGION"],
+))
+app.synth()

infra/cdk.json

@@ -0,0 +1,3 @@
+{
+  "app": "python3 app.py"
+}

infra/requirements.txt

@@ -0,0 +1,2 @@
+aws-cdk-lib>=2.170.0
+constructs>=10.0.0

infra/stack.py

@@ -0,0 +1,213 @@
+import aws_cdk as cdk
+from aws_cdk import (
+    Stack,
+    Duration,
+    RemovalPolicy,
+    aws_s3 as s3,
+    aws_ecs as ecs,
+    aws_ec2 as ec2,
+    aws_ecr_assets,
+    aws_iam as iam,
+    aws_logs as logs,
+    aws_stepfunctions as sfn,
+    aws_stepfunctions_tasks as sfn_tasks,
+)
+from constructs import Construct
+from pathlib import Path
+
+
+class AdsbProcessingStack(Stack):
+    def __init__(self, scope: Construct, id: str, **kwargs):
+        super().__init__(scope, id, **kwargs)
+
+        # --- S3 bucket for intermediate and final results ---
+        bucket = s3.Bucket(
+            self, "ResultsBucket",
+            bucket_name="planequery-aircraft-dev",
+            removal_policy=RemovalPolicy.DESTROY,
+            auto_delete_objects=True,
+            lifecycle_rules=[
+                s3.LifecycleRule(
+                    prefix="intermediate/",
+                    expiration=Duration.days(7),
+                )
+            ],
+        )
+
+        # --- Use default VPC (no additional cost) ---
+        vpc = ec2.Vpc.from_lookup(
+            self, "Vpc",
+            is_default=True,
+        )
+
+        # --- ECS Cluster ---
+        cluster = ecs.Cluster(
+            self, "Cluster",
+            vpc=vpc,
+            container_insights=True,
+        )
+
+        # --- Log group ---
+        log_group = logs.LogGroup(
+            self, "LogGroup",
+            log_group_name="/adsb-processing",
+            removal_policy=RemovalPolicy.DESTROY,
+            retention=logs.RetentionDays.TWO_WEEKS,
+        )
+
+        # --- Docker images (built from local Dockerfiles) ---
+        adsb_dir = str(Path(__file__).parent.parent / "src" / "adsb")
+
+        worker_image = ecs.ContainerImage.from_asset(
+            adsb_dir,
+            file="Dockerfile.worker",
+            platform=cdk.aws_ecr_assets.Platform.LINUX_ARM64,
+        )
+        reducer_image = ecs.ContainerImage.from_asset(
+            adsb_dir,
+            file="Dockerfile.reducer",
+            platform=cdk.aws_ecr_assets.Platform.LINUX_ARM64,
+        )
+
+        # --- Task role (shared) ---
+        task_role = iam.Role(
+            self, "TaskRole",
+            assumed_by=iam.ServicePrincipal("ecs-tasks.amazonaws.com"),
+        )
+        bucket.grant_read_write(task_role)
+
+        # --- MAP: worker task definition ---
+        map_task_def = ecs.FargateTaskDefinition(
+            self, "MapTaskDef",
+            cpu=4096,           # 4 vCPU
+            memory_limit_mib=30720,  # 30 GB
+            task_role=task_role,
+            runtime_platform=ecs.RuntimePlatform(
+                cpu_architecture=ecs.CpuArchitecture.ARM64,
+                operating_system_family=ecs.OperatingSystemFamily.LINUX,
+            ),
+        )
+        map_container = map_task_def.add_container(
+            "worker",
+            image=worker_image,
+            logging=ecs.LogDrivers.aws_logs(
+                stream_prefix="map",
+                log_group=log_group,
+            ),
+            environment={
+                "S3_BUCKET": bucket.bucket_name,
+            },
+        )
+
+        # --- REDUCE: reducer task definition ---
+        reduce_task_def = ecs.FargateTaskDefinition(
+            self, "ReduceTaskDef",
+            cpu=4096,            # 4 vCPU
+            memory_limit_mib=30720,  # 30 GB — must hold full year in memory
+            task_role=task_role,
+            runtime_platform=ecs.RuntimePlatform(
+                cpu_architecture=ecs.CpuArchitecture.ARM64,
+                operating_system_family=ecs.OperatingSystemFamily.LINUX,
+            ),
+        )
+        reduce_container = reduce_task_def.add_container(
+            "reducer",
+            image=reducer_image,
+            logging=ecs.LogDrivers.aws_logs(
+                stream_prefix="reduce",
+                log_group=log_group,
+            ),
+            environment={
+                "S3_BUCKET": bucket.bucket_name,
+            },
+        )
+
+        # --- Step Functions ---
+
+        # Map task: run ECS Fargate for each date chunk
+        map_ecs_task = sfn_tasks.EcsRunTask(
+            self, "ProcessChunk",
+            integration_pattern=sfn.IntegrationPattern.RUN_JOB,
+            cluster=cluster,
+            task_definition=map_task_def,
+            launch_target=sfn_tasks.EcsFargateLaunchTarget(
+                platform_version=ecs.FargatePlatformVersion.LATEST,
+            ),
+            container_overrides=[
+                sfn_tasks.ContainerOverride(
+                    container_definition=map_container,
+                    environment=[
+                        sfn_tasks.TaskEnvironmentVariable(
+                            name="START_DATE",
+                            value=sfn.JsonPath.string_at("$.start_date"),
+                        ),
+                        sfn_tasks.TaskEnvironmentVariable(
+                            name="END_DATE",
+                            value=sfn.JsonPath.string_at("$.end_date"),
+                        ),
+                        sfn_tasks.TaskEnvironmentVariable(
+                            name="RUN_ID",
+                            value=sfn.JsonPath.string_at("$.run_id"),
+                        ),
+                    ],
+                )
+            ],
+            assign_public_ip=True,
+            subnets=ec2.SubnetSelection(subnet_type=ec2.SubnetType.PUBLIC),
+            result_path="$.task_result",
+        )
+
+        # Map state — max 3 concurrent workers
+        map_state = sfn.Map(
+            self, "FanOutChunks",
+            items_path="$.chunks",
+            max_concurrency=3,
+            result_path="$.map_results",
+        )
+        map_state.item_processor(map_ecs_task)
+
+        # Reduce task: combine all chunk CSVs
+        reduce_ecs_task = sfn_tasks.EcsRunTask(
+            self, "ReduceResults",
+            integration_pattern=sfn.IntegrationPattern.RUN_JOB,
+            cluster=cluster,
+            task_definition=reduce_task_def,
+            launch_target=sfn_tasks.EcsFargateLaunchTarget(
+                platform_version=ecs.FargatePlatformVersion.LATEST,
+            ),
+            container_overrides=[
+                sfn_tasks.ContainerOverride(
+                    container_definition=reduce_container,
+                    environment=[
+                        sfn_tasks.TaskEnvironmentVariable(
+                            name="RUN_ID",
+                            value=sfn.JsonPath.string_at("$.run_id"),
+                        ),
+                        sfn_tasks.TaskEnvironmentVariable(
+                            name="GLOBAL_START_DATE",
+                            value=sfn.JsonPath.string_at("$.global_start_date"),
+                        ),
+                        sfn_tasks.TaskEnvironmentVariable(
+                            name="GLOBAL_END_DATE",
+                            value=sfn.JsonPath.string_at("$.global_end_date"),
+                        ),
+                    ],
+                )
+            ],
+            assign_public_ip=True,
+            subnets=ec2.SubnetSelection(subnet_type=ec2.SubnetType.PUBLIC),
+        )
+
+        # Chain: fan-out map → reduce
+        definition = map_state.next(reduce_ecs_task)
+
+        sfn.StateMachine(
+            self, "Pipeline",
+            state_machine_name="adsb-map-reduce",
+            definition_body=sfn.DefinitionBody.from_chainable(definition),
+            timeout=Duration.hours(48),
+        )
+
+        # --- Outputs ---
+        cdk.CfnOutput(self, "BucketName", value=bucket.bucket_name)
+        cdk.CfnOutput(self, "StateMachineName", value="adsb-map-reduce")

notebooks/planequery_adsb_read.ipynb

@@ -0,0 +1,640 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06ae0319",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import clickhouse_connect\n",
+    "client = clickhouse_connect.get_client(\n",
+    "    host=os.environ[\"CLICKHOUSE_HOST\"],\n",
+    "    username=os.environ[\"CLICKHOUSE_USERNAME\"],\n",
+    "    password=os.environ[\"CLICKHOUSE_PASSWORD\"],\n",
+    "    secure=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "779710f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = client.query_df(\"SELECT time, icao,r,t,dbFlags,ownOp,year,desc,aircraft FROM adsb_messages Where time > '2024-01-01 00:00:00' AND time < '2024-01-02 00:00:00'\")\n",
+    "df_copy = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf024da8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# -- military = dbFlags & 1; interesting = dbFlags & 2; PIA = dbFlags & 4; LADD = dbFlags & 8;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270607b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = load_raw_adsb_for_day(datetime(2024,1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac06a30e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['aircraft']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91edab3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COLUMNS = ['dbFlags', 'ownOp', 'year', 'desc', 'aircraft_category', 'r', 't']\n",
+    "def compress_df(df):\n",
+    "    icao = df.name\n",
+    "    df[\"_signature\"] = df[COLUMNS].astype(str).agg('|'.join, axis=1)\n",
+    "    original_df = df.copy()\n",
+    "    df = df.groupby(\"_signature\", as_index=False).last() # check if it works with both last and first.\n",
+    "    # For each row, create a dict of non-empty column values. This is using sets and subsets...\n",
+    "    def get_non_empty_dict(row):\n",
+    "        return {col: row[col] for col in COLUMNS if row[col] != ''}\n",
+    "    \n",
+    "    df['_non_empty_dict'] = df.apply(get_non_empty_dict, axis=1)\n",
+    "    df['_non_empty_count'] = df['_non_empty_dict'].apply(len)\n",
+    "    \n",
+    "    # Check if row i's non-empty values are a subset of row j's non-empty values\n",
+    "    def is_subset_of_any(idx):\n",
+    "        row_dict = df.loc[idx, '_non_empty_dict']\n",
+    "        row_count = df.loc[idx, '_non_empty_count']\n",
+    "        \n",
+    "        for other_idx in df.index:\n",
+    "            if idx == other_idx:\n",
+    "                continue\n",
+    "            other_dict = df.loc[other_idx, '_non_empty_dict']\n",
+    "            other_count = df.loc[other_idx, '_non_empty_count']\n",
+    "            \n",
+    "            # Check if all non-empty values in current row match those in other row\n",
+    "            if all(row_dict.get(k) == other_dict.get(k) for k in row_dict.keys()):\n",
+    "                # If they match and other has more defined columns, current row is redundant\n",
+    "                if other_count > row_count:\n",
+    "                    return True\n",
+    "        return False\n",
+    "    \n",
+    "    # Keep rows that are not subsets of any other row\n",
+    "    keep_mask = ~df.index.to_series().apply(is_subset_of_any)\n",
+    "    df = df[keep_mask]\n",
+    "\n",
+    "    if len(df) > 1:\n",
+    "        original_df = original_df[original_df['_signature'].isin(df['_signature'])]\n",
+    "        value_counts = original_df[\"_signature\"].value_counts()\n",
+    "        max_signature = value_counts.idxmax()\n",
+    "        df = df[df['_signature'] == max_signature]\n",
+    "\n",
+    "    df['icao'] = icao\n",
+    "    df = df.drop(columns=['_non_empty_dict', '_non_empty_count', '_signature'])\n",
+    "    return df\n",
+    "\n",
+    "# df = df_copy\n",
+    "# df = df_copy.iloc[0:100000]\n",
+    "# df = df[df['r'] == \"N4131T\"]\n",
+    "# df = df[(df['icao'] == \"008081\")]\n",
+    "# df = df.iloc[0:500]\n",
+    "df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "df = df.drop(columns=['aircraft'])\n",
+    "df = df.sort_values(['icao', 'time'])\n",
+    "df[COLUMNS] = df[COLUMNS].fillna('')\n",
+    "ORIGINAL_COLUMNS = df.columns.tolist()\n",
+    "df_compressed = df.groupby('icao',group_keys=False).apply(compress_df)\n",
+    "cols = df_compressed.columns.tolist()\n",
+    "cols.remove(\"icao\")\n",
+    "cols.insert(1, \"icao\")\n",
+    "df_compressed = df_compressed[cols]\n",
+    "df_compressed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efdfcb2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "df[~df['aircraft_category'].isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "495c5025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SOME KIND OF MAP REDUCE SYSTEM\n",
+    "import os\n",
+    "\n",
+    "COLUMNS = ['dbFlags', 'ownOp', 'year', 'desc', 'aircraft_category', 'r', 't']\n",
+    "def compress_df(df):\n",
+    "    icao = df.name\n",
+    "    df[\"_signature\"] = df[COLUMNS].astype(str).agg('|'.join, axis=1)\n",
+    "    \n",
+    "    # Compute signature counts before grouping (avoid copy)\n",
+    "    signature_counts = df[\"_signature\"].value_counts()\n",
+    "    \n",
+    "    df = df.groupby(\"_signature\", as_index=False).first() # check if it works with both last and first.\n",
+    "    # For each row, create a dict of non-empty column values. This is using sets and subsets...\n",
+    "    def get_non_empty_dict(row):\n",
+    "        return {col: row[col] for col in COLUMNS if row[col] != ''}\n",
+    "    \n",
+    "    df['_non_empty_dict'] = df.apply(get_non_empty_dict, axis=1)\n",
+    "    df['_non_empty_count'] = df['_non_empty_dict'].apply(len)\n",
+    "    \n",
+    "    # Check if row i's non-empty values are a subset of row j's non-empty values\n",
+    "    def is_subset_of_any(idx):\n",
+    "        row_dict = df.loc[idx, '_non_empty_dict']\n",
+    "        row_count = df.loc[idx, '_non_empty_count']\n",
+    "        \n",
+    "        for other_idx in df.index:\n",
+    "            if idx == other_idx:\n",
+    "                continue\n",
+    "            other_dict = df.loc[other_idx, '_non_empty_dict']\n",
+    "            other_count = df.loc[other_idx, '_non_empty_count']\n",
+    "            \n",
+    "            # Check if all non-empty values in current row match those in other row\n",
+    "            if all(row_dict.get(k) == other_dict.get(k) for k in row_dict.keys()):\n",
+    "                # If they match and other has more defined columns, current row is redundant\n",
+    "                if other_count > row_count:\n",
+    "                    return True\n",
+    "        return False\n",
+    "    \n",
+    "    # Keep rows that are not subsets of any other row\n",
+    "    keep_mask = ~df.index.to_series().apply(is_subset_of_any)\n",
+    "    df = df[keep_mask]\n",
+    "\n",
+    "    if len(df) > 1:\n",
+    "        # Use pre-computed signature counts instead of original_df\n",
+    "        remaining_sigs = df['_signature']\n",
+    "        sig_counts = signature_counts[remaining_sigs]\n",
+    "        max_signature = sig_counts.idxmax()\n",
+    "        df = df[df['_signature'] == max_signature]\n",
+    "\n",
+    "    df['icao'] = icao\n",
+    "    df = df.drop(columns=['_non_empty_dict', '_non_empty_count', '_signature'])\n",
+    "    return df\n",
+    "\n",
+    "# names of releases something like\n",
+    "# planequery_aircraft_adsb_2024-06-01T00-00-00Z.csv.gz\n",
+    "\n",
+    "# Let's build historical first. \n",
+    "\n",
+    "_ch_client = None\n",
+    "\n",
+    "def _get_clickhouse_client():\n",
+    "    \"\"\"Return a reusable ClickHouse client, with retry/backoff for transient DNS or connection errors.\"\"\"\n",
+    "    global _ch_client\n",
+    "    if _ch_client is not None:\n",
+    "        return _ch_client\n",
+    "\n",
+    "    import clickhouse_connect\n",
+    "    import time\n",
+    "\n",
+    "    max_retries = 5\n",
+    "    for attempt in range(1, max_retries + 1):\n",
+    "        try:\n",
+    "            _ch_client = clickhouse_connect.get_client(\n",
+    "                host=os.environ[\"CLICKHOUSE_HOST\"],\n",
+    "                username=os.environ[\"CLICKHOUSE_USERNAME\"],\n",
+    "                password=os.environ[\"CLICKHOUSE_PASSWORD\"],\n",
+    "                secure=True,\n",
+    "            )\n",
+    "            return _ch_client\n",
+    "        except Exception as e:\n",
+    "            wait = min(2 ** attempt, 30)\n",
+    "            print(f\"  ClickHouse connect attempt {attempt}/{max_retries} failed: {e}\")\n",
+    "            if attempt == max_retries:\n",
+    "                raise\n",
+    "            print(f\"  Retrying in {wait}s...\")\n",
+    "            time.sleep(wait)\n",
+    "\n",
+    "\n",
+    "def load_raw_adsb_for_day(day):\n",
+    "    \"\"\"Load raw ADS-B data for a day from cache or ClickHouse.\"\"\"\n",
+    "    from datetime import timedelta\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    import time\n",
+    "    \n",
+    "    start_time = day.replace(hour=0, minute=0, second=0, microsecond=0)\n",
+    "    end_time = start_time + timedelta(days=1)\n",
+    "    \n",
+    "    # Set up caching\n",
+    "    cache_dir = Path(\"data/adsb\")\n",
+    "    cache_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    cache_file = cache_dir / f\"adsb_raw_{start_time.strftime('%Y-%m-%d')}.csv.zst\"\n",
+    "    \n",
+    "    # Check if cache exists\n",
+    "    if cache_file.exists():\n",
+    "        print(f\"  Loading from cache: {cache_file}\")\n",
+    "        df = pd.read_csv(cache_file, compression='zstd')\n",
+    "        df['time'] = pd.to_datetime(df['time'])\n",
+    "    else:\n",
+    "        # Format dates for the query\n",
+    "        start_str = start_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        end_str = end_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        \n",
+    "        max_retries = 3\n",
+    "        for attempt in range(1, max_retries + 1):\n",
+    "            try:\n",
+    "                client = _get_clickhouse_client()\n",
+    "                print(f\"  Querying ClickHouse for {start_time.strftime('%Y-%m-%d')}\")\n",
+    "                df = client.query_df(f\"SELECT time, icao,r,t,dbFlags,ownOp,year,desc,aircraft FROM adsb_messages Where time > '{start_str}' AND time < '{end_str}'\")\n",
+    "                break\n",
+    "            except Exception as e:\n",
+    "                wait = min(2 ** attempt, 30)\n",
+    "                print(f\"  Query attempt {attempt}/{max_retries} failed: {e}\")\n",
+    "                if attempt == max_retries:\n",
+    "                    raise\n",
+    "                # Reset client in case connection is stale\n",
+    "                global _ch_client\n",
+    "                _ch_client = None\n",
+    "                print(f\"  Retrying in {wait}s...\")\n",
+    "                time.sleep(wait)\n",
+    "        \n",
+    "        # Save to cache\n",
+    "        df.to_csv(cache_file, index=False, compression='zstd')\n",
+    "        print(f\"  Saved to cache: {cache_file}\")\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def load_historical_for_day(day):\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    df = load_raw_adsb_for_day(day)\n",
+    "    print(df)\n",
+    "    df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "    df = df.drop(columns=['aircraft'])\n",
+    "    df = df.sort_values(['icao', 'time'])\n",
+    "    df[COLUMNS] = df[COLUMNS].fillna('')\n",
+    "    df_compressed = df.groupby('icao',group_keys=False).apply(compress_df)\n",
+    "    cols = df_compressed.columns.tolist()\n",
+    "    cols.remove('time')\n",
+    "    cols.insert(0, 'time')\n",
+    "    cols.remove(\"icao\")\n",
+    "    cols.insert(1, \"icao\")\n",
+    "    df_compressed = df_compressed[cols]\n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def concat_compressed_dfs(df_base, df_new):\n",
+    "    \"\"\"Concatenate base and new compressed dataframes, keeping the most informative row per ICAO.\"\"\"\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    # Combine both dataframes\n",
+    "    df_combined = pd.concat([df_base, df_new], ignore_index=True)\n",
+    "    \n",
+    "    # Sort by ICAO and time\n",
+    "    df_combined = df_combined.sort_values(['icao', 'time'])\n",
+    "    \n",
+    "    # Fill NaN values\n",
+    "    df_combined[COLUMNS] = df_combined[COLUMNS].fillna('')\n",
+    "    \n",
+    "    # Apply compression logic per ICAO to get the best row\n",
+    "    df_compressed = df_combined.groupby('icao', group_keys=False).apply(compress_df)\n",
+    "    \n",
+    "    # Sort by time\n",
+    "    df_compressed = df_compressed.sort_values('time')\n",
+    "    \n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def get_latest_aircraft_adsb_csv_df():\n",
+    "    \"\"\"Download and load the latest ADS-B CSV from GitHub releases.\"\"\"\n",
+    "    from get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv\n",
+    "    \n",
+    "    import pandas as pd\n",
+    "    import re\n",
+    "    \n",
+    "    csv_path = download_latest_aircraft_adsb_csv()\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    df = df.fillna(\"\")\n",
+    "    \n",
+    "    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv\n",
+    "    match = re.search(r\"planequery_aircraft_adsb_(\\d{4}-\\d{2}-\\d{2})_\", str(csv_path))\n",
+    "    if not match:\n",
+    "        raise ValueError(f\"Could not extract date from filename: {csv_path.name}\")\n",
+    "    \n",
+    "    date_str = match.group(1)\n",
+    "    return df, date_str\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f66acf7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SOME KIND OF MAP REDUCE SYSTEM\n",
+    "\n",
+    "\n",
+    "COLUMNS = ['dbFlags', 'ownOp', 'year', 'desc', 'aircraft_category', 'r', 't']\n",
+    "def compress_df(df):\n",
+    "    icao = df.name\n",
+    "    df[\"_signature\"] = df[COLUMNS].astype(str).agg('|'.join, axis=1)\n",
+    "    original_df = df.copy()\n",
+    "    df = df.groupby(\"_signature\", as_index=False).first() # check if it works with both last and first.\n",
+    "    # For each row, create a dict of non-empty column values. This is using sets and subsets...\n",
+    "    def get_non_empty_dict(row):\n",
+    "        return {col: row[col] for col in COLUMNS if row[col] != ''}\n",
+    "    \n",
+    "    df['_non_empty_dict'] = df.apply(get_non_empty_dict, axis=1)\n",
+    "    df['_non_empty_count'] = df['_non_empty_dict'].apply(len)\n",
+    "    \n",
+    "    # Check if row i's non-empty values are a subset of row j's non-empty values\n",
+    "    def is_subset_of_any(idx):\n",
+    "        row_dict = df.loc[idx, '_non_empty_dict']\n",
+    "        row_count = df.loc[idx, '_non_empty_count']\n",
+    "        \n",
+    "        for other_idx in df.index:\n",
+    "            if idx == other_idx:\n",
+    "                continue\n",
+    "            other_dict = df.loc[other_idx, '_non_empty_dict']\n",
+    "            other_count = df.loc[other_idx, '_non_empty_count']\n",
+    "            \n",
+    "            # Check if all non-empty values in current row match those in other row\n",
+    "            if all(row_dict.get(k) == other_dict.get(k) for k in row_dict.keys()):\n",
+    "                # If they match and other has more defined columns, current row is redundant\n",
+    "                if other_count > row_count:\n",
+    "                    return True\n",
+    "        return False\n",
+    "    \n",
+    "    # Keep rows that are not subsets of any other row\n",
+    "    keep_mask = ~df.index.to_series().apply(is_subset_of_any)\n",
+    "    df = df[keep_mask]\n",
+    "\n",
+    "    if len(df) > 1:\n",
+    "        original_df = original_df[original_df['_signature'].isin(df['_signature'])]\n",
+    "        value_counts = original_df[\"_signature\"].value_counts()\n",
+    "        max_signature = value_counts.idxmax()\n",
+    "        df = df[df['_signature'] == max_signature]\n",
+    "\n",
+    "    df['icao'] = icao\n",
+    "    df = df.drop(columns=['_non_empty_dict', '_non_empty_count', '_signature'])\n",
+    "    return df\n",
+    "\n",
+    "# names of releases something like\n",
+    "# planequery_aircraft_adsb_2024-06-01T00-00-00Z.csv.gz\n",
+    "\n",
+    "# Let's build historical first. \n",
+    "\n",
+    "def load_raw_adsb_for_day(day):\n",
+    "    \"\"\"Load raw ADS-B data for a day from cache or ClickHouse.\"\"\"\n",
+    "    from datetime import timedelta\n",
+    "    import clickhouse_connect\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    start_time = day.replace(hour=0, minute=0, second=0, microsecond=0)\n",
+    "    end_time = start_time + timedelta(days=1)\n",
+    "    \n",
+    "    # Set up caching\n",
+    "    cache_dir = Path(\"data/adsb\")\n",
+    "    cache_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    cache_file = cache_dir / f\"adsb_raw_{start_time.strftime('%Y-%m-%d')}.csv.zst\"\n",
+    "    \n",
+    "    # Check if cache exists\n",
+    "    if cache_file.exists():\n",
+    "        print(f\"  Loading from cache: {cache_file}\")\n",
+    "        df = pd.read_csv(cache_file, compression='zstd')\n",
+    "        df['time'] = pd.to_datetime(df['time'])\n",
+    "    else:\n",
+    "        # Format dates for the query\n",
+    "        start_str = start_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        end_str = end_time.strftime('%Y-%m-%d %H:%M:%S')\n",
+    "        \n",
+    "        client = clickhouse_connect.get_client(\n",
+    "            host=os.environ[\"CLICKHOUSE_HOST\"],\n",
+    "            username=os.environ[\"CLICKHOUSE_USERNAME\"],\n",
+    "            password=os.environ[\"CLICKHOUSE_PASSWORD\"],\n",
+    "            secure=True,\n",
+    "        )\n",
+    "        print(f\"  Querying ClickHouse for {start_time.strftime('%Y-%m-%d')}\")\n",
+    "        df = client.query_df(f\"SELECT time, icao,r,t,dbFlags,ownOp,year,desc,aircraft FROM adsb_messages Where time > '{start_str}' AND time < '{end_str}'\")\n",
+    "        \n",
+    "        # Save to cache\n",
+    "        df.to_csv(cache_file, index=False, compression='zstd')\n",
+    "        print(f\"  Saved to cache: {cache_file}\")\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def load_historical_for_day(day):\n",
+    "    from pathlib import Path\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    df = load_raw_adsb_for_day(day)\n",
+    "    \n",
+    "    df['aircraft_category'] = df['aircraft'].apply(lambda x: x.get('category') if isinstance(x, dict) else None)\n",
+    "    df = df.drop(columns=['aircraft'])\n",
+    "    df = df.sort_values(['icao', 'time'])\n",
+    "    df[COLUMNS] = df[COLUMNS].fillna('')\n",
+    "    df_compressed = df.groupby('icao',group_keys=False).apply(compress_df)\n",
+    "    cols = df_compressed.columns.tolist()\n",
+    "    cols.remove('time')\n",
+    "    cols.insert(0, 'time')\n",
+    "    cols.remove(\"icao\")\n",
+    "    cols.insert(1, \"icao\")\n",
+    "    df_compressed = df_compressed[cols]\n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def concat_compressed_dfs(df_base, df_new):\n",
+    "    \"\"\"Concatenate base and new compressed dataframes, keeping the most informative row per ICAO.\"\"\"\n",
+    "    import pandas as pd\n",
+    "    \n",
+    "    # Combine both dataframes\n",
+    "    df_combined = pd.concat([df_base, df_new], ignore_index=True)\n",
+    "    \n",
+    "    # Sort by ICAO and time\n",
+    "    df_combined = df_combined.sort_values(['icao', 'time'])\n",
+    "    \n",
+    "    # Fill NaN values\n",
+    "    df_combined[COLUMNS] = df_combined[COLUMNS].fillna('')\n",
+    "    \n",
+    "    # Apply compression logic per ICAO to get the best row\n",
+    "    df_compressed = df_combined.groupby('icao', group_keys=False).apply(compress_df)\n",
+    "    \n",
+    "    # Sort by time\n",
+    "    df_compressed = df_compressed.sort_values('time')\n",
+    "    \n",
+    "    return df_compressed\n",
+    "\n",
+    "\n",
+    "def get_latest_aircraft_adsb_csv_df():\n",
+    "    \"\"\"Download and load the latest ADS-B CSV from GitHub releases.\"\"\"\n",
+    "    from get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv\n",
+    "    \n",
+    "    import pandas as pd\n",
+    "    import re\n",
+    "    \n",
+    "    csv_path = download_latest_aircraft_adsb_csv()\n",
+    "    df = pd.read_csv(csv_path)\n",
+    "    df = df.fillna(\"\")\n",
+    "    \n",
+    "    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv\n",
+    "    match = re.search(r\"planequery_aircraft_adsb_(\\d{4}-\\d{2}-\\d{2})_\", str(csv_path))\n",
+    "    if not match:\n",
+    "        raise ValueError(f\"Could not extract date from filename: {csv_path.name}\")\n",
+    "    \n",
+    "    date_str = match.group(1)\n",
+    "    return df, date_str\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e14c8363",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datetime import datetime\n",
+    "df = load_historical_for_day(datetime(2024,1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3874ba4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcae50ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[(df['icao'] == \"008081\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50921c86",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df['icao'] == \"a4e1d2\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8194d9aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[df['r'] == \"N4131T\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e3b7aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_compressed[df_compressed['icao'].duplicated(keep=False)]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40613bc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gzip\n",
+    "import json\n",
+    "\n",
+    "path = \"/Users/jonahgoode/Downloads/test_extract/traces/fb/trace_full_acbbfb.json\"\n",
+    "\n",
+    "with gzip.open(path, \"rt\", encoding=\"utf-8\") as f:\n",
+    "    data = json.load(f)\n",
+    "\n",
+    "print(type(data))\n",
+    "# use `data` here\n",
+    "import json\n",
+    "print(json.dumps(data, indent=2)[:2000])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "320109b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First, load the JSON to inspect its structure\n",
+    "import json\n",
+    "with open(\"/Users/jonahgoode/Documents/PlaneQuery/Other-Code/readsb-protobuf/webapp/src/db/aircrafts.json\", 'r') as f:\n",
+    "    data = json.load(f)\n",
+    "\n",
+    "# Check the structure\n",
+    "print(type(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "590134f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data['AC97E3']"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

schemas/community_submission.v1.schema.json

@@ -1,6 +1,6 @@
 {
   "$schema": "https://json-schema.org/draft/2020-12/schema",
-  "title": "OpenAirframes Community Submission (v1)",
+  "title": "PlaneQuery Aircraft Community Submission (v1)",
   "type": "object",
   "additionalProperties": false,
   "properties": {
@@ -12,7 +12,7 @@
       "type": "string",
       "pattern": "^[0-9A-F]{6}$"
     },
-    "openairframes_id": {
+    "planequery_airframe_id": {
       "type": "string",
       "minLength": 1
     },
@@ -46,15 +46,22 @@
     },
     "tags": {
       "type": "object",
-      "description": "Additional community-defined tags as key/value pairs (values may be scalar, array, or object).",
+      "description": "Community-defined tags. New tags can be added, but must use consistent types.",
       "propertyNames": {
         "type": "string",
         "pattern": "^[a-z][a-z0-9_]{0,63}$"
       },
+      "properties": {
+        "internet": {
+          "type": "string"
+        },
+        "owner": {
+          "type": "string"
+        }
+      },
       "additionalProperties": {
         "$ref": "#/$defs/tagValue"
-      },
-      "properties": {}
+      }
     }
   },
   "allOf": [
@@ -72,7 +79,7 @@
         },
         {
           "required": [
-            "openairframes_id"
+            "planequery_airframe_id"
           ]
         }
       ]

src/adsb/combine_chunks_to_csv.py

@@ -27,7 +27,7 @@ from src.adsb.compress_adsb_to_aircraft_data import compress_multi_icao_df, COLU
 
 
 DEFAULT_CHUNK_DIR = os.path.join(OUTPUT_DIR, "adsb_chunks")
-FINAL_OUTPUT_DIR = "./data/openairframes"
+FINAL_OUTPUT_DIR = "./data/planequery_aircraft"
 os.makedirs(FINAL_OUTPUT_DIR, exist_ok=True)
 
 
@@ -85,12 +85,12 @@ def combine_compressed_chunks(compressed_dfs: list[pl.DataFrame]) -> pl.DataFram
 
 def download_and_merge_base_release(compressed_df: pl.DataFrame) -> pl.DataFrame:
     """Download base release and merge with new data."""
-    from src.get_latest_release import download_latest_aircraft_adsb_csv
+    from src.get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv
     
     print("Downloading base ADS-B release...")
     try:
         base_path = download_latest_aircraft_adsb_csv(
-            output_dir="./data/openairframes_base"
+            output_dir="./data/planequery_aircraft_base"
         )
         print(f"Download returned: {base_path}")
         
@@ -176,10 +176,10 @@ def main():
     if args.start_date and args.end_date:
         # Historical mode
         output_id = f"{args.start_date}_{args.end_date}"
-        output_filename = f"openairframes_adsb_{args.start_date}_{args.end_date}.csv"
+        output_filename = f"planequery_aircraft_adsb_{args.start_date}_{args.end_date}.csv"
         print(f"Combining chunks for date range: {args.start_date} to {args.end_date}")
     else:
-        # Daily mode - use same date for start and end
+        # Daily mode
         if args.date:
             target_day = datetime.strptime(args.date, "%Y-%m-%d")
         else:
@@ -187,7 +187,7 @@ def main():
         
         date_str = target_day.strftime("%Y-%m-%d")
         output_id = date_str
-        output_filename = f"openairframes_adsb_{date_str}_{date_str}.csv"
+        output_filename = f"planequery_aircraft_adsb_{date_str}.csv"
         print(f"Combining chunks for {date_str}")
     
     chunks_dir = args.chunks_dir

src/adsb/compress_adsb_to_aircraft_data.py

@@ -253,7 +253,7 @@ def concat_compressed_dfs(df_base, df_new):
 
 def get_latest_aircraft_adsb_csv_df():
     """Download and load the latest ADS-B CSV from GitHub releases."""
-    from get_latest_release import download_latest_aircraft_adsb_csv
+    from get_latest_planequery_aircraft_release import download_latest_aircraft_adsb_csv
     import re
     
     csv_path = download_latest_aircraft_adsb_csv()
@@ -264,8 +264,8 @@ def get_latest_aircraft_adsb_csv_df():
         if df[col].dtype == pl.Utf8:
             df = df.with_columns(pl.col(col).fill_null(""))
     
-    # Extract start date from filename pattern: openairframes_adsb_{start_date}_{end_date}.csv
-    match = re.search(r"openairframes_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv
+    match = re.search(r"planequery_aircraft_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
         raise ValueError(f"Could not extract date from filename: {csv_path.name}")
     

src/adsb/download_adsb_data_to_parquet.py

@@ -82,8 +82,7 @@ def fetch_releases(version_date: str) -> list:
     if version_date == "v2024.12.31":
         year = "2025"
     BASE_URL = f"https://api.github.com/repos/adsblol/globe_history_{year}/releases"
-    # Match exact release name, exclude tmp releases
-    PATTERN = rf"^{re.escape(version_date)}-planes-readsb-prod-\d+$"
+    PATTERN = f"{version_date}-planes-readsb-prod-0"
     releases = []
     page = 1
     
@@ -188,23 +187,19 @@ def extract_split_archive(file_paths: list, extract_dir: str) -> bool:
         cat_proc = subprocess.Popen(
             ["cat"] + file_paths,
             stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE
+            stderr=subprocess.DEVNULL
         )
         tar_cmd = ["tar", "xf", "-", "-C", extract_dir, "--strip-components=1"]
-        result = subprocess.run(
+        subprocess.run(
             tar_cmd,
             stdin=cat_proc.stdout,
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
+            stdout=subprocess.DEVNULL,
+            stderr=subprocess.DEVNULL,
             check=True
         )
         cat_proc.stdout.close()
-        cat_stderr = cat_proc.stderr.read().decode() if cat_proc.stderr else ""
         cat_proc.wait()
         
-        if cat_stderr:
-            print(f"cat stderr: {cat_stderr}")
-        
         print(f"Successfully extracted archive to {extract_dir}")
         
         # Delete tar files immediately after extraction
@@ -222,10 +217,7 @@ def extract_split_archive(file_paths: list, extract_dir: str) -> bool:
         
         return True
     except subprocess.CalledProcessError as e:
-        stderr_output = e.stderr.decode() if e.stderr else ""
         print(f"Failed to extract split archive: {e}")
-        if stderr_output:
-            print(f"tar stderr: {stderr_output}")
         return False
 
 

src/adsb/reducer.py

@@ -76,8 +76,8 @@ def main():
     print(f"After dedup: {df_accumulated.height} rows")
 
     # Write and upload final result
-    output_name = f"openairframes_adsb_{global_start}_{global_end}.csv.gz"
-    csv_output = Path(f"/tmp/openairframes_adsb_{global_start}_{global_end}.csv")
+    output_name = f"planequery_aircraft_adsb_{global_start}_{global_end}.csv.gz"
+    csv_output = Path(f"/tmp/planequery_aircraft_adsb_{global_start}_{global_end}.csv")
     gz_output = Path(f"/tmp/{output_name}")
     
     df_accumulated.write_csv(csv_output)

src/contributions/create_daily_community_release.py

@@ -17,7 +17,7 @@ import pandas as pd
 
 
 COMMUNITY_DIR = Path(__file__).parent.parent.parent / "community"
-OUT_ROOT = Path("data/openairframes")
+OUT_ROOT = Path("data/planequery_aircraft")
 
 
 def read_all_submissions(community_dir: Path) -> list[dict]:
@@ -47,7 +47,7 @@ def submissions_to_dataframe(submissions: list[dict]) -> pd.DataFrame:
     - creation_timestamp (first)
     - transponder_code_hex
     - registration_number  
-    - openairframes_id
+    - planequery_airframe_id
     - contributor_name
     - [other columns alphabetically]
     - contributor_uuid (last)
@@ -62,7 +62,7 @@ def submissions_to_dataframe(submissions: list[dict]) -> pd.DataFrame:
         "creation_timestamp",
         "transponder_code_hex",
         "registration_number",
-        "openairframes_id",
+        "planequery_airframe_id",
         "contributor_name",
         "contributor_uuid",
     ]
@@ -78,7 +78,7 @@ def submissions_to_dataframe(submissions: list[dict]) -> pd.DataFrame:
         "creation_timestamp",
         "transponder_code_hex",
         "registration_number",
-        "openairframes_id",
+        "planequery_airframe_id",
         "contributor_name",
     ]
     last_cols = ["contributor_uuid"]
@@ -108,7 +108,7 @@ def main():
             "creation_timestamp",
             "transponder_code_hex",
             "registration_number",
-            "openairframes_id",
+            "planequery_airframe_id",
             "contributor_name",
             "tags",
             "contributor_uuid",
@@ -127,7 +127,7 @@ def main():
     
     # Output
     OUT_ROOT.mkdir(parents=True, exist_ok=True)
-    output_file = OUT_ROOT / f"openairframes_community_{start_date_str}_{date_str}.csv"
+    output_file = OUT_ROOT / f"planequery_aircraft_community_{start_date_str}_{date_str}.csv"
     
     df.to_csv(output_file, index=False)
     

src/contributions/read_community_data.py

@@ -112,8 +112,8 @@ def group_by_identifier(submissions: list[dict]) -> dict[str, list[dict]]:
             key = f"reg:{submission['registration_number']}"
         elif "transponder_code_hex" in submission:
             key = f"icao:{submission['transponder_code_hex']}"
-        elif "openairframes_id" in submission:
-            key = f"id:{submission['openairframes_id']}"
+        elif "planequery_airframe_id" in submission:
+            key = f"id:{submission['planequery_airframe_id']}"
         else:
             key = "_unknown"
         

src/contributions/schema.py

@@ -111,7 +111,7 @@ def download_github_attachment(url: str) -> str | None:
     import urllib.error
     
     try:
-        req = urllib.request.Request(url, headers={"User-Agent": "OpenAirframes-Bot"})
+        req = urllib.request.Request(url, headers={"User-Agent": "PlaneQuery-Bot"})
         with urllib.request.urlopen(req, timeout=30) as response:
             return response.read().decode("utf-8")
     except (urllib.error.URLError, urllib.error.HTTPError, UnicodeDecodeError) as e:

src/contributions/update_schema.py

@@ -58,9 +58,18 @@ def generate_updated_schema(base_schema: dict, tag_registry: dict[str, str]) ->
     for tag_name, type_name in sorted(tag_registry.items()):
         tag_properties[tag_name] = type_name_to_json_schema(type_name)
     
-    # Only add/update the properties key within tags, preserve everything else
-    if "properties" in schema and "tags" in schema["properties"]:
-        schema["properties"]["tags"]["properties"] = tag_properties
+    # Update tags definition
+    schema["properties"]["tags"] = {
+        "type": "object",
+        "description": "Community-defined tags. New tags can be added, but must use consistent types.",
+        "propertyNames": {
+            "type": "string",
+            "pattern": "^[a-z][a-z0-9_]{0,63}$"
+        },
+        "properties": tag_properties,
+        # Still allow additional properties for new tags
+        "additionalProperties": {"$ref": "#/$defs/tagValue"}
+    }
     
     return schema
 

src/create_daily_faa_release.py

@@ -1,49 +0,0 @@
-from pathlib import Path
-from datetime import datetime, timezone, timedelta
-import argparse
-
-parser = argparse.ArgumentParser(description="Create daily FAA release")
-parser.add_argument("--date", type=str, help="Date to process (YYYY-MM-DD format, default: today)")
-args = parser.parse_args()
-
-if args.date:
-    date_str = args.date
-else:
-    date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
-
-out_dir = Path("data/faa_releasable")
-out_dir.mkdir(parents=True, exist_ok=True)
-zip_name = f"ReleasableAircraft_{date_str}.zip"
-
-zip_path = out_dir / zip_name
-if not zip_path.exists():
-    # URL and paths
-    url = "https://registry.faa.gov/database/ReleasableAircraft.zip"
-    from urllib.request import Request, urlopen
-
-    req = Request(
-        url,
-        headers={"User-Agent": "Mozilla/5.0"},
-        method="GET",
-    )
-
-    with urlopen(req, timeout=120) as r:
-        body = r.read()
-        zip_path.write_bytes(body)
-
-OUT_ROOT = Path("data/openairframes")
-OUT_ROOT.mkdir(parents=True, exist_ok=True)
-from derive_from_faa_master_txt import convert_faa_master_txt_to_df, concat_faa_historical_df
-from get_latest_release import get_latest_aircraft_faa_csv_df
-df_new = convert_faa_master_txt_to_df(zip_path, date_str)
-
-try:
-    df_base, start_date_str = get_latest_aircraft_faa_csv_df()
-    df_base = concat_faa_historical_df(df_base, df_new)
-    assert df_base['download_date'].is_monotonic_increasing, "download_date is not monotonic increasing"
-except Exception as e:
-    print(f"No existing FAA release found, using only new data: {e}")
-    df_base = df_new
-    start_date_str = date_str
-
-df_base.to_csv(OUT_ROOT / f"openairframes_faa_{start_date_str}_{date_str}.csv", index=False)

src/create_daily_planequery_aircraft_adsb_release.py

@@ -74,10 +74,10 @@ if __name__ == '__main__':
             )
 
     # Save the result
-    OUT_ROOT = Path("data/openairframes")
+    OUT_ROOT = Path("data/planequery_aircraft")
     OUT_ROOT.mkdir(parents=True, exist_ok=True)
 
-    output_file = OUT_ROOT / f"openairframes_adsb_{start_date_str}_{date_str}.csv"
+    output_file = OUT_ROOT / f"planequery_aircraft_adsb_{start_date_str}_{date_str}.csv"
     df_combined.write_csv(output_file)
 
     print(f"Saved: {output_file}")

src/create_daily_planequery_aircraft_release.py

@@ -0,0 +1,33 @@
+from pathlib import Path
+from datetime import datetime, timezone
+date_str = datetime.now(timezone.utc).strftime("%Y-%m-%d")
+
+out_dir = Path("data/faa_releasable")
+out_dir.mkdir(parents=True, exist_ok=True)
+zip_name = f"ReleasableAircraft_{date_str}.zip"
+
+zip_path = out_dir / zip_name
+if not zip_path.exists():
+    # URL and paths
+    url = "https://registry.faa.gov/database/ReleasableAircraft.zip"
+    from urllib.request import Request, urlopen
+
+    req = Request(
+        url,
+        headers={"User-Agent": "Mozilla/5.0"},
+        method="GET",
+    )
+
+    with urlopen(req, timeout=120) as r:
+        body = r.read()
+        zip_path.write_bytes(body)
+
+OUT_ROOT = Path("data/planequery_aircraft")
+OUT_ROOT.mkdir(parents=True, exist_ok=True)
+from derive_from_faa_master_txt import convert_faa_master_txt_to_df, concat_faa_historical_df
+from get_latest_planequery_aircraft_release import get_latest_aircraft_faa_csv_df
+df_new = convert_faa_master_txt_to_df(zip_path, date_str)
+df_base, start_date_str = get_latest_aircraft_faa_csv_df()
+df_base = concat_faa_historical_df(df_base, df_new)
+assert df_base['download_date'].is_monotonic_increasing, "download_date is not monotonic increasing"
+df_base.to_csv(OUT_ROOT / f"planequery_aircraft_faa_{start_date_str}_{date_str}.csv", index=False)

src/derive_from_faa_master_txt.py

@@ -29,8 +29,8 @@ def convert_faa_master_txt_to_df(zip_path: Path, date: str):
     certification = pd.json_normalize(df["certification"].where(df["certification"].notna(), {})).add_prefix("certificate_")
     df = df.drop(columns="certification").join(certification)
     
-    # Create openairframes_id
-    df["openairframes_id"] = (
+    # Create planequery_airframe_id
+    df["planequery_airframe_id"] = (
         normalize(df["aircraft_manufacturer"])
         + "|"
         + normalize(df["aircraft_model"])
@@ -38,11 +38,11 @@ def convert_faa_master_txt_to_df(zip_path: Path, date: str):
         + normalize(df["serial_number"])
     )
     
-    # Move openairframes_id to come after registration_number
+    # Move planequery_airframe_id to come after registration_number
     cols = df.columns.tolist()
-    cols.remove("openairframes_id")
+    cols.remove("planequery_airframe_id")
     reg_idx = cols.index("registration_number")
-    cols.insert(reg_idx + 1, "openairframes_id")
+    cols.insert(reg_idx + 1, "planequery_airframe_id")
     df = df[cols]
     
     # Convert all NaN to empty strings

src/get_latest_planequery_aircraft_release.py

@@ -9,7 +9,7 @@ import urllib.error
 import json
 
 
-REPO = "PlaneQuery/openairframes"
+REPO = "PlaneQuery/planequery-aircraft"
 LATEST_RELEASE_URL = f"https://api.github.com/repos/{REPO}/releases/latest"
 
 
@@ -31,7 +31,7 @@ def get_latest_release_assets(repo: str = REPO, github_token: Optional[str] = No
     url = f"https://api.github.com/repos/{repo}/releases/latest"
     headers = {
         "Accept": "application/vnd.github+json",
-        "User-Agent": "openairframes-downloader/1.0",
+        "User-Agent": "planequery-aircraft-downloader/1.0",
     }
     if github_token:
         headers["Authorization"] = f"Bearer {github_token}"
@@ -80,7 +80,7 @@ def download_asset(asset: ReleaseAsset, out_path: Path, github_token: Optional[s
     out_path.parent.mkdir(parents=True, exist_ok=True)
 
     headers = {
-        "User-Agent": "openairframes-downloader/1.0",
+        "User-Agent": "planequery-aircraft-downloader/1.0",
         "Accept": "application/octet-stream",
     }
     if github_token:
@@ -109,7 +109,7 @@ def download_latest_aircraft_csv(
     repo: str = REPO,
 ) -> Path:
     """
-    Download the latest openairframes_faa_*.csv file from the latest GitHub release.
+    Download the latest planequery_aircraft_faa_*.csv file from the latest GitHub release.
 
     Args:
         output_dir: Directory to save the downloaded file (default: "downloads")
@@ -121,10 +121,10 @@ def download_latest_aircraft_csv(
     """
     assets = get_latest_release_assets(repo, github_token=github_token)
     try:
-        asset = pick_asset(assets, name_regex=r"^openairframes_faa_.*\.csv$")
+        asset = pick_asset(assets, name_regex=r"^planequery_aircraft_faa_.*\.csv$")
     except FileNotFoundError:
         # Fallback to old naming pattern
-        asset = pick_asset(assets, name_regex=r"^openairframes_\d{4}-\d{2}-\d{2}_.*\.csv$")
+        asset = pick_asset(assets, name_regex=r"^planequery_aircraft_\d{4}-\d{2}-\d{2}_.*\.csv$")
     saved_to = download_asset(asset, output_dir / asset.name, github_token=github_token)
     print(f"Downloaded: {asset.name} ({asset.size} bytes) -> {saved_to}")
     return saved_to
@@ -136,11 +136,11 @@ def get_latest_aircraft_faa_csv_df():
            'unique_regulatory_id': str, 
            'registrant_county': str})
     df = df.fillna("")
-    # Extract start date from filename pattern: openairframes_faa_{start_date}_{end_date}.csv
-    match = re.search(r"openairframes_faa_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+    # Extract start date from filename pattern: planequery_aircraft_faa_{start_date}_{end_date}.csv
+    match = re.search(r"planequery_aircraft_faa_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
-        # Fallback to old naming pattern: openairframes_{start_date}_{end_date}.csv
-        match = re.search(r"openairframes_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+        # Fallback to old naming pattern: planequery_aircraft_{start_date}_{end_date}.csv
+        match = re.search(r"planequery_aircraft_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
         raise ValueError(f"Could not extract date from filename: {csv_path.name}")
     
@@ -154,7 +154,7 @@ def download_latest_aircraft_adsb_csv(
     repo: str = REPO,
 ) -> Path:
     """
-    Download the latest openairframes_adsb_*.csv file from the latest GitHub release.
+    Download the latest planequery_aircraft_adsb_*.csv file from the latest GitHub release.
 
     Args:
         output_dir: Directory to save the downloaded file (default: "downloads")
@@ -165,7 +165,7 @@ def download_latest_aircraft_adsb_csv(
         Path to the downloaded file
     """
     assets = get_latest_release_assets(repo, github_token=github_token)
-    asset = pick_asset(assets, name_regex=r"^openairframes_adsb_.*\.csv$")
+    asset = pick_asset(assets, name_regex=r"^planequery_aircraft_adsb_.*\.csv$")
     saved_to = download_asset(asset, output_dir / asset.name, github_token=github_token)
     print(f"Downloaded: {asset.name} ({asset.size} bytes) -> {saved_to}")
     return saved_to
@@ -176,8 +176,8 @@ def get_latest_aircraft_adsb_csv_df():
     import pandas as pd
     df = pd.read_csv(csv_path)
     df = df.fillna("")
-    # Extract start date from filename pattern: openairframes_adsb_{start_date}_{end_date}.csv
-    match = re.search(r"openairframes_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
+    # Extract start date from filename pattern: planequery_aircraft_adsb_{start_date}_{end_date}.csv
+    match = re.search(r"planequery_aircraft_adsb_(\d{4}-\d{2}-\d{2})_", str(csv_path))
     if not match:
         raise ValueError(f"Could not extract date from filename: {csv_path.name}")
     

trigger_pipeline.py

@@ -0,0 +1,90 @@
+"""
+Generate Step Functions input and start the pipeline.
+
+Usage:
+  python trigger_pipeline.py 2024-01-01 2025-01-01
+  python trigger_pipeline.py 2024-01-01 2025-01-01 --chunk-days 30
+  python trigger_pipeline.py 2024-01-01 2025-01-01 --dry-run
+"""
+import argparse
+import json
+import os
+import uuid
+from datetime import datetime, timedelta
+
+import boto3
+
+
+def generate_chunks(start_date: str, end_date: str, chunk_days: int = 1):
+    """Split a date range into chunks of chunk_days."""
+    start = datetime.strptime(start_date, "%Y-%m-%d")
+    end = datetime.strptime(end_date, "%Y-%m-%d")
+
+    chunks = []
+    current = start
+    while current < end:
+        chunk_end = min(current + timedelta(days=chunk_days), end)
+        chunks.append({
+            "start_date": current.strftime("%Y-%m-%d"),
+            "end_date": chunk_end.strftime("%Y-%m-%d"),
+        })
+        current = chunk_end
+
+    return chunks
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Trigger ADS-B map-reduce pipeline")
+    parser.add_argument("start_date", help="Start date (YYYY-MM-DD, inclusive)")
+    parser.add_argument("end_date", help="End date (YYYY-MM-DD, exclusive)")
+    parser.add_argument("--chunk-days", type=int, default=1,
+                        help="Days per chunk (default: 1)")
+    parser.add_argument("--dry-run", action="store_true",
+                        help="Print input JSON without starting execution")
+    args = parser.parse_args()
+
+    run_id = f"run-{datetime.utcnow().strftime('%Y%m%dT%H%M%S')}-{uuid.uuid4().hex[:8]}"
+    chunks = generate_chunks(args.start_date, args.end_date, args.chunk_days)
+
+    # Inject run_id into each chunk
+    for chunk in chunks:
+        chunk["run_id"] = run_id
+
+    sfn_input = {
+        "run_id": run_id,
+        "global_start_date": args.start_date,
+        "global_end_date": args.end_date,
+        "chunks": chunks,
+    }
+
+    print(f"Run ID:    {run_id}")
+    print(f"Chunks:    {len(chunks)} (at {args.chunk_days} days each)")
+    print(f"Max concurrency: 3 (enforced by Step Functions Map state)")
+    print()
+    print(json.dumps(sfn_input, indent=2))
+
+    if args.dry_run:
+        print("\n--dry-run: not starting execution")
+        return
+
+    client = boto3.client("stepfunctions")
+
+    # Find the state machine ARN
+    machines = client.list_state_machines()["stateMachines"]
+    arn = next(
+        m["stateMachineArn"]
+        for m in machines
+        if m["name"] == "adsb-map-reduce"
+    )
+
+    response = client.start_execution(
+        stateMachineArn=arn,
+        name=run_id,
+        input=json.dumps(sfn_input),
+    )
+
+    print(f"\nStarted execution: {response['executionArn']}")
+
+
+if __name__ == "__main__":
+    main()