Atmospheric Intelligence System (AIS)

Refactored architecture (April 2026):

Iceberg is the historical source of truth.
Cassandra is serving storage for low-latency queries.
Realtime/near-realtime sources run as long-running Spark Structured Streaming jobs.
Airflow is used for orchestration, supervision, backfill, and maintenance.

Trạng thái dự án (2026-05-27)

Bảng dưới đây phản ánh mức độ triển khai theo TODO trong repo hiện tại (code + manifest đã có):

Hạng mục	Trạng thái	Ghi chú
Core pipeline (ingest -> Kafka -> Spark -> Iceberg -> Cassandra)	Đã triển khai	Ingest + streaming vào Iceberg bronze, Cassandra serving weather/openaq, Airflow DAGs + Monitoring UI.
TODO1 - Hanoi PM2.5 silver/gold	Đã triển khai	`config/hanoi_pipeline.yaml`, `spark_jobs/hanoi_config.py`, các job silver/gold + script `scripts/run_todo1_end_to_end.sh`.
TODO2 - Tier-2 trajectory	Đã triển khai	ERA5 pressure-level ingest, HYSPLIT run/parse/cluster, trajectory feature jobs.
TODO3 - Kubernetes compute layer	Đang hoàn thiện	`deploy/k8s`, `scripts/submit_spark_k8s.sh`, DAG `ais_pm25_k8s_compute`, ML train/predict jobs, PM2.5 API. Cần hardening + vận hành production.
UI	Demo	UI hiện dùng mock data (chưa nối data production).

1. Refactored architecture

1.1 Architecture diagram

flowchart LR
  subgraph Sources
    W[Weather API / local weather history]
    O[OpenAQ API]
    S[Sentinel-5P metadata API]
    M[MAIAC metadata API]
  end

  subgraph Ingestion
    IW[weather ingest]
    IO[openaq ingest]
    IS[sentinel5p ingest]
    IM[maiac ingest]
  end

  subgraph Streaming_Batch_Compute
    K[(Kafka)]
    SW[weather streaming processor]
    SO[openaq streaming processor]
    SS[sentinel5p streaming processor]
    SM[maiac processor]
  end

  subgraph Storage
    I[(Iceberg on HDFS)]
    C[(Cassandra serving)]
  end

  subgraph Control
    A[Airflow DAGs]
    MON[Monitoring UI]
  end

  W --> IW --> K
  O --> IO --> K
  S --> IS --> K
  M --> IM --> K

  K --> SW --> I
  K --> SO --> I
  K --> SS --> I
  K --> SM --> I

  I --> C

  A --> IW
  A --> IO
  A --> IS
  A --> IM
  A --> SW
  A --> SO
  A --> SS
  A --> SM
  A --> I
  A --> C

  MON --> A
  MON --> K
  MON --> I

1.2 Data flow diagram

flowchart TD
  A[Source events] --> B[Ingest adapters]
  B --> C[Kafka topics]
  C --> D1[Weather stream]
  C --> D2[OpenAQ stream]
  C --> D3[Sentinel-5P stream]
  C --> D4[MAIAC stream/backfill]

  D1 --> E[Iceberg weather.weather_history_bronze]
  D2 --> F[Iceberg air_quality.openaq_hourly_bronze]
  D3 --> G[Iceberg satellite.sentinel5p_summary_bronze]
  D4 --> H[Iceberg satellite.maiac_summary_bronze]

  E --> I1[Cassandra weather_hourly_by_province_day]
  F --> I2[Cassandra openaq_hourly_by_city_parameter_day]

1.3 DAG responsibility diagram

flowchart LR
  D1[ais_batch_orchestration]
  D2[ais_streaming_supervision]
  D3[ais_maiac_backfill]
  D4[ais_maintenance]

  D1 --> R1[Historical bootstrap]
  D1 --> R2[One-shot processing to Iceberg]
  D1 --> R3[Serving refresh to Cassandra]

  D2 --> R4[Ensure topics/tables/schemas]
  D2 --> R5[Start/check/restart streaming jobs]
  D2 --> R6[Kafka lag checks]

  D3 --> R7[Delayed MAIAC ingest]
  D3 --> R8[Backfill MAIAC to Iceberg]

  D4 --> R9[Iceberg rewrite data files]
  D4 --> R10[Expire snapshots/orphan cleanup]
  D4 --> R11[Iceberg vs Cassandra reconciliation]

2. Pipeline paths

2.1 Historical bootstrap

Implemented by DAG ais_batch_orchestration:

Ensure Kafka topics.
Ensure Iceberg namespaces/tables.
Run batch ingest for weather, openaq, sentinel5p, maiac.
Run one-shot Spark catchup for each source (--stop-after-batch 1) to persist into Iceberg.
Refresh weather/openaq serving tables in Cassandra.

2.2 Streaming supervision

Implemented by DAG ais_streaming_supervision:

Ensure topics/tables/schema.
Ensure long-running streaming processors are running (start if missing).
Check Kafka lag for stream consumer groups.

2.3 MAIAC delayed backfill

Implemented by DAG ais_maiac_backfill:

Pull delayed MAIAC metadata in batch windows.
Process to Iceberg via one-shot Spark catchup.
Optional serving refresh hook (currently no MAIAC Cassandra serving table is defined).

2.4 Maintenance and validation

Implemented by DAG ais_maintenance:

rewrite_data_files compact pass.
expire_snapshots and remove_orphan_files.
Reconciliation check Iceberg vs Cassandra for weather/openaq.

3. Key modules (refactored)

Orchestration

airflow/dags/ais_pipeline_dag.py: bootstrap/historical load DAG (kept existing DAG id).
airflow/dags/ais_streaming_supervision_dag.py: stream supervision DAG.
airflow/dags/ais_maiac_backfill_dag.py: MAIAC delayed backfill DAG.
airflow/dags/ais_maintenance_dag.py: maintenance/reconciliation DAG.
airflow/dags/ais_dag_utils.py: shared command builders used by DAGs.

Ingestion

ingest/ingest_weather.py
ingest/openaq_ingest.py
ingest/sentinel5p_ingest.py
ingest/maiac_ingest.py
Shared:
ingest/kafka_utils.py
ingest/window_utils.py

Processing and storage

spark_jobs/weather_streaming.py
spark_jobs/openaq_hourly_streaming.py
spark_jobs/sentinel5p_summary_streaming.py
spark_jobs/maiac_summary_streaming.py
spark_jobs/iceberg_to_cassandra.py
spark_jobs/ensure_iceberg_tables.py
spark_jobs/iceberg_maintenance.py
spark_jobs/reconcile_iceberg_cassandra.py
spark_jobs/runtime_utils.py

Supervision scripts

scripts/airflow/ensure_stream_job.sh
scripts/airflow/check_kafka_lag.sh
scripts/submit_spark.sh
scripts/backfill_all_sources.sh
scripts/run_infrastructure_only.sh

4. Local/dev runbook

4.1 Start infrastructure

bash scripts/run_infrastructure_only.sh

This script starts:

Kafka, HDFS, Spark, Cassandra
Iceberg table ensure job
long-running Spark processors (detached)
Airflow services
monitoring UI

4.2 Trigger historical bootstrap

Option A (UI):

Open http://localhost:8501
Click Start 7-Day Backfill DAG

Option B (Airflow CLI/API):

Trigger DAG ais_batch_orchestration with conf lookback_days.

Option C (manual script):

LOOKBACK_DAYS=7 bash scripts/backfill_all_sources.sh

4.3 Streaming operation

Streaming jobs are expected to stay long-running:

WeatherHistory_Streaming
OpenAQHourly_Streaming
Sentinel5PSummary_Streaming
MAIACSummary_Streaming

Supervision DAG ais_streaming_supervision ensures these jobs remain up.

4.4 MAIAC backfill

Use DAG ais_maiac_backfill (daily schedule) or trigger manually in Airflow for ad-hoc windows.

4.5 Maintenance

Use DAG ais_maintenance for compaction/snapshot expiration/reconciliation.

5. Monitoring

Monitoring UI: http://localhost:8501
Airflow UI: http://localhost:8088
Spark master UI: http://localhost:8080
HDFS UI: http://localhost:9870

Monitoring now checks persisted data under Iceberg warehouse path by default.

6. Migration notes

6.1 Major changes

Sentinel-5P and MAIAC processors now persist to Iceberg tables (not only parquet path sinks).
Weather/OpenAQ processors now run long-running by default and support bootstrap mode via --stop-after-batch 1.
Airflow orchestration split by responsibility into bootstrap, supervision, backfill, maintenance DAGs.
Added lag checks and stream auto-start checks for supervision.
Added Iceberg maintenance and Iceberg-vs-Cassandra reconciliation jobs.

6.2 Backward compatibility

Existing DAG id ais_batch_orchestration is preserved.
Existing ingest modules and Kafka topic names are preserved.
Existing serving tables in Cassandra for weather/openaq are preserved.

6.3 Operational cautions

Cassandra remains serving storage, not historical source.
If Kafka consumer groups are still warming up, lag checks may return warnings until first commits.
MAIAC Cassandra serving table is intentionally not introduced yet; MAIAC is persisted in Iceberg and can be projected later if required.

Atmospheric Intelligence System (AIS)

Pipeline Big Data cho dữ liệu khí quyển: WeatherAPI history, OpenAQ hourly, Sentinel-5P và MAIAC. Kiến trúc vận hành chuẩn: Python ingest adapters -> Kafka -> Spark (realtime/batch) -> Iceberg/HDFS -> Cassandra, với Airflow chỉ giữ vai trò batch orchestration.

Mục lục

Tài liệu dataset liên quan

Xem README_DATASETS.md để biết chi tiết schema, định dạng và cách khai thác OpenAQ, WeatherAPI, Sentinel-5P (NetCDF) và Mosaic MAIAC (HDF4).

1. Kiến trúc tổng thể

Pipeline Flow

Weather JSON/API ─┐
OpenAQ CSV/API  ──┼──> Python Ingest ──> Kafka ──> Spark Structured Streaming ──> Iceberg/HDFS
Sentinel-5P API ──┘                               │
                                                   └──> Spark batch load ──> Cassandra

Airflow điều phối các bước batch ingest và batch load serving (khong chay long-running realtime jobs).
Monitoring UI đọc Kafka/HDFS để theo dõi throughput và trạng thái lưu trữ.

Vai trò từng service

Service	Vai trò
`zookeeper`	Quản lý cluster metadata cho Kafka
`kafka`	Message broker nhận events từ ingest và cung cấp cho Spark
`namenode`, `datanode`	HDFS storage cho warehouse Iceberg và checkpoint
`spark-master`, `spark-worker`	Chạy Spark Structured Streaming và batch jobs
`ingest`, `openaq-ingest`, `sentinel5p-ingest`, `maiac-ingest`	Python source adapters đẩy dữ liệu về Kafka
`cassandra`	Serving layer cho truy vấn latency thấp
`airflow-*`	Airflow metadata DB, webserver, scheduler, triggerer và DAG orchestration
`monitoring-ui`	Dashboard theo dõi Kafka, HDFS/DataNode và pipeline status

2. Cấu trúc thư mục

Atmospheric_intelligence_sys---AIS/
├── docker-compose.yml              # Orchestration local cho Kafka, HDFS, Spark, Cassandra, Airflow, monitoring
├── airflow/
│   ├── Dockerfile
│   └── dags/
│       └── ais_pipeline_dag.py     # DAG batch orchestration cho ingest + load Cassandra
├── ingest/
│   ├── Dockerfile
│   ├── requirements.txt
│   ├── ingest_weather.py           # Weather history producer
│   ├── openaq_ingest.py            # OpenAQ hourly producer
│   ├── sentinel5p_ingest.py        # Sentinel-5P summary producer
│   └── maiac_ingest.py             # MAIAC metadata producer
├── spark_jobs/
│   ├── weather_streaming.py        # Kafka weather_history -> Iceberg
│   ├── openaq_hourly_streaming.py  # Kafka openaq-hourly -> Iceberg
│   ├── sentinel5p_summary_streaming.py # Kafka sentinel5p-summary -> HDFS parquet
│   ├── maiac_summary_streaming.py  # Kafka maiac-summary -> HDFS parquet
│   └── iceberg_to_cassandra.py     # Iceberg -> Cassandra serving tables
├── data/
│   ├── weather/                    # Weather history JSON theo tỉnh/thành
│   └── crawling/                   # Script crawl WeatherAPI, OpenAQ, Sentinel-5P
├── crawler/                        # GeoJSON, notebook và dữ liệu MODIS MAIAC
├── monitoring/                     # Monitoring UI
├── scripts/                        # Helper scripts tạo topic, submit Spark, health check
├── hadoop/
│   └── hadoop.env                  # Cấu hình Hadoop/HDFS
└── checkpoints/                    # Runtime state/checkpoint

3. Docker Compose

File docker-compose.yml chạy các service trên network chung bigdata-net.

Các cổng chính:

Port	Service	Mô tả
2181	Zookeeper	Client connections
9092 / 29092	Kafka	Internal / external listeners
9870	HDFS Namenode	Web UI
9864	HDFS Datanode	Web UI
8080	Spark Master	Web UI
7077	Spark Master	RPC
8088	Airflow Webserver	Airflow UI
9042	Cassandra	CQL
8501	Monitoring UI	Pipeline dashboard

Persistent volumes:

namenode_data, datanode_data: HDFS data
cassandra_data: Cassandra data
airflow_postgres_data: Airflow metadata database

NiFi khong con nam trong runtime path chinh. Folder nifi/ duoc giu lai cho future work.

4. Ingest services

Weather history

File: ingest/ingest_weather.py
Kafka topic mặc định: weather_history
Input:
- Local JSON trong ./data/weather/<province>/<date>.json, hoặc
- WeatherAPI history khi cấu hình mode/API key phù hợp
Output: mỗi bản ghi theo giờ là một JSON event gồm event_id, province, query_date, event_time, nhiệt độ, độ ẩm, gió, mưa, điều kiện thời tiết, tọa độ và metadata ingest.

OpenAQ hourly

File: ingest/openaq_ingest.py
Kafka topic mặc định: openaq-hourly
Input mặc định trong container: /data/crawling/openaq_vietnam_hourly.csv
Output: mỗi dòng hourly measurement thành một JSON event gồm location, sensor, parameter, unit, value, min/max/sd, coverage và metadata ingest.

Sentinel-5P summary

File: ingest/sentinel5p_ingest.py
Service Compose: sentinel5p-ingest
Kafka topic mặc định: sentinel5p-summary
Input: CDSE credentials từ biến môi trường CDSE_USERNAME, CDSE_PASSWORD
Output: summary statistics cho các product NO2, CO, O3, SO2, CH4, AER trong bbox cấu hình.

5. Spark và storage

Streaming jobs

Job	Kafka topic	Iceberg table	Checkpoint
`weather_streaming.py`	`weather_history`	`ais.weather.weather_history_bronze`	`hdfs://namenode:9000/checkpoints/weather_history/`
`openaq_hourly_streaming.py`	`openaq-hourly`	`ais.air_quality.openaq_hourly_bronze`	`hdfs://namenode:9000/checkpoints/openaq_hourly/`

Summary streaming jobs (realtime path):

Job	Kafka topic	Sink
`sentinel5p_summary_streaming.py`	`sentinel5p-summary`	`ais.satellite.sentinel5p_summary_bronze`
`maiac_summary_streaming.py`	`maiac-summary`	`ais.satellite.maiac_summary_bronze`

Iceberg warehouse:

hdfs://namenode:9000/warehouse/iceberg

Cassandra serving

spark_jobs/iceberg_to_cassandra.py đọc từ Iceberg và ghi sang keyspace ais_serving:

weather_hourly_by_province_day
openaq_hourly_by_city_parameter_day

6. Hướng dẫn chạy

Yêu cầu

Docker Desktop hoặc Docker Engine
Docker Compose v2
Tối thiểu 8 GB RAM cho Docker
Tối thiểu 10 GB disk trống

1. Khởi động infrastructure

docker-compose up -d zookeeper kafka namenode datanode spark-master spark-worker cassandra
docker-compose ps

UI hữu ích:

HDFS Namenode: http://localhost:9870
Spark Master: http://localhost:8080
Cassandra: localhost:9042

2. Tạo Kafka topics

docker exec kafka kafka-topics --create --bootstrap-server kafka:9092 --replication-factor 1 --partitions 3 --topic weather_history --if-not-exists
docker exec kafka kafka-topics --create --bootstrap-server kafka:9092 --replication-factor 1 --partitions 3 --topic openaq-hourly --if-not-exists
docker exec kafka kafka-topics --create --bootstrap-server kafka:9092 --replication-factor 1 --partitions 3 --topic sentinel5p-summary --if-not-exists
docker exec kafka kafka-topics --create --bootstrap-server kafka:9092 --replication-factor 1 --partitions 3 --topic maiac-summary --if-not-exists
docker exec kafka kafka-topics --list --bootstrap-server kafka:9092

3. Tạo HDFS paths

docker exec namenode hdfs dfs -mkdir -p /warehouse/iceberg
docker exec namenode hdfs dfs -mkdir -p /checkpoints/weather_history
docker exec namenode hdfs dfs -mkdir -p /checkpoints/openaq_hourly
docker exec namenode hdfs dfs -chmod -R 777 /warehouse
docker exec namenode hdfs dfs -chmod -R 777 /checkpoints

4. Chạy ingest

docker compose build ingest openaq-ingest sentinel5p-ingest maiac-ingest
docker compose run --rm -e WINDOW_MODE=batch -e BATCH_LOOKBACK_DAYS=7 ingest
docker compose run --rm -e WINDOW_MODE=batch -e BATCH_LOOKBACK_DAYS=7 openaq-ingest

Sentinel-5P va MAIAC co the chay rieng:

docker compose run --rm -e WINDOW_MODE=batch -e BATCH_LOOKBACK_DAYS=7 sentinel5p-ingest
docker compose run --rm -e WINDOW_MODE=batch -e BATCH_LOOKBACK_DAYS=30 maiac-ingest

5. Submit Spark jobs

bash scripts/submit_spark.sh weather
bash scripts/submit_spark.sh openaq
bash scripts/submit_spark.sh sentinel5p
bash scripts/submit_spark.sh maiac

Sau khi có dữ liệu trong Iceberg, load sang Cassandra:

bash scripts/submit_spark.sh cassandra-weather
bash scripts/submit_spark.sh cassandra-openaq

6. Chạy Airflow orchestration

docker compose up --build airflow-init
docker compose up -d airflow-webserver airflow-scheduler airflow-triggerer

Airflow UI: http://localhost:8088

Đăng nhập mặc định:

Username: admin
Password: admin

DAG chính: ais_batch_orchestration

7. Mở Monitoring UI

docker-compose up -d --build monitoring-ui

Monitoring UI: http://localhost:8501

7. Kiểm tra kết quả

Kafka

docker exec kafka kafka-run-class kafka.tools.GetOffsetShell --broker-list kafka:9092 --topic weather_history --time -1
docker exec kafka kafka-run-class kafka.tools.GetOffsetShell --broker-list kafka:9092 --topic openaq-hourly --time -1
docker exec kafka kafka-run-class kafka.tools.GetOffsetShell --broker-list kafka:9092 --topic sentinel5p-summary --time -1
docker exec kafka kafka-run-class kafka.tools.GetOffsetShell --broker-list kafka:9092 --topic maiac-summary --time -1

docker exec kafka kafka-console-consumer --bootstrap-server kafka:9092 --topic weather_history --from-beginning --max-messages 5
docker exec kafka kafka-console-consumer --bootstrap-server kafka:9092 --topic openaq-hourly --from-beginning --max-messages 5

Spark

docker logs spark-master -f --tail 50

Mở Spark UI tại http://localhost:8080 và kiểm tra các application:

WeatherHistory_Streaming
OpenAQHourly_Streaming
Sentinel5PSummary_Streaming
MAIACSummary_Streaming
IcebergToCassandra_Weather
IcebergToCassandra_OpenAQ

HDFS / Iceberg

docker exec namenode hdfs dfs -ls -R /warehouse/iceberg
docker exec namenode hdfs dfs -ls -R /checkpoints/weather_history
docker exec namenode hdfs dfs -ls -R /checkpoints/openaq_hourly

Mở HDFS Web UI: http://localhost:9870 -> Utilities -> Browse the file system -> /warehouse/iceberg.

Cassandra

8. Lưu ý thiết kế cho Kubernetes

TODO3 đặt Kubernetes làm runtime đích cho compute, không migrate storage/stateful data trong pha này.

Giữ Kafka/Zookeeper, HDFS/Iceberg warehouse, Cassandra và Airflow metadata DB ở tầng storage/data infrastructure bên ngoài Kubernetes.
Docker Compose Spark master/worker chỉ là fallback local/dev; Spark driver/executor pods trên Kubernetes mới là target runtime của TODO3.
Airflow tiếp tục là control plane để schedule/backfill/rerun, còn Kubernetes thực thi Spark batch, HYSPLIT/trajectory, ML training, ML inference, API và check jobs.
Config runtime đi qua environment variables, ConfigMap hoặc Secret; không hardcode hostname Compose trong code path chạy trên Kubernetes.
Pod Kubernetes phải dùng endpoint được override qua KAFKA_BOOTSTRAP_SERVERS, HDFS_NAMENODE, HDFS_WEBHDFS_BASE, ICEBERG_WAREHOUSE, CASSANDRA_HOST khi cần.

Luồng dữ liệu đích của TODO3:

External sources
-> ingest producers
-> Kafka/HDFS/Iceberg storage layer
-> Spark jobs on Kubernetes
-> Iceberg Bronze/Silver/Gold
-> Hanoi PM2.5 master hourly gold
-> PM2.5 serving features gold
-> ML inference Job/CronJob on Kubernetes
-> PM2.5 prediction table
-> PM2.5 API Deployment on Kubernetes
-> dashboard/user

Request-time API chỉ đọc prediction đã materialize và trả JSON. API không submit Spark, không chạy HYSPLIT, không build feature, không train model và không chạy inference trong handler.

Dọn dẹp

docker-compose down
docker-compose down -v
docker-compose down --rmi local

Name		Name	Last commit message	Last commit date
Latest commit History 111 Commits
airflow		airflow
checkpoints		checkpoints
config		config
crawler		crawler
data		data
deploy/k8s		deploy/k8s
description		description
docs		docs
hadoop		hadoop
ingest		ingest
ml		ml
monitoring		monitoring
notebooks		notebooks
scripts		scripts
serving		serving
spark		spark
spark_jobs		spark_jobs
ui		ui
.dockerignore		.dockerignore
.env.example		.env.example
.gitattributes		.gitattributes
.gitignore		.gitignore
PROJECT_FULL_NOTES.md		PROJECT_FULL_NOTES.md
QUICKSTART.md		QUICKSTART.md
README.md		README.md
README_DATASETS.md		README_DATASETS.md
README_HDFS_ICEBERG.md		README_HDFS_ICEBERG.md
SYSTEM_OVERVIEW.md		SYSTEM_OVERVIEW.md
TODO_1.md		TODO_1.md
TODO_2.md		TODO_2.md
TODO_3.md		TODO_3.md
TODO_4.md		TODO_4.md
docker-compose.yml		docker-compose.yml

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Atmospheric Intelligence System (AIS)

Trạng thái dự án (2026-05-27)

1. Refactored architecture

1.1 Architecture diagram

1.2 Data flow diagram

1.3 DAG responsibility diagram

2. Pipeline paths

2.1 Historical bootstrap

2.2 Streaming supervision

2.3 MAIAC delayed backfill

2.4 Maintenance and validation

3. Key modules (refactored)

Orchestration

Ingestion

Processing and storage

Supervision scripts

4. Local/dev runbook

4.1 Start infrastructure

4.2 Trigger historical bootstrap

4.3 Streaming operation

4.4 MAIAC backfill

4.5 Maintenance

5. Monitoring

6. Migration notes

6.1 Major changes

6.2 Backward compatibility

6.3 Operational cautions

Atmospheric Intelligence System (AIS)

Mục lục

Tài liệu dataset liên quan

1. Kiến trúc tổng thể

Pipeline Flow

Vai trò từng service

2. Cấu trúc thư mục

3. Docker Compose

4. Ingest services

Weather history

OpenAQ hourly

Sentinel-5P summary

5. Spark và storage

Streaming jobs

Cassandra serving

6. Hướng dẫn chạy

Yêu cầu

1. Khởi động infrastructure

2. Tạo Kafka topics

3. Tạo HDFS paths

4. Chạy ingest

5. Submit Spark jobs

6. Chạy Airflow orchestration

7. Mở Monitoring UI

7. Kiểm tra kết quả

Kafka

Spark

HDFS / Iceberg

Cassandra

8. Lưu ý thiết kế cho Kubernetes

Dọn dẹp

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