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beast

(★ 150)

[Deprecated] Load data from Kafka to any data warehouse. BQ sink is being supported in Firehose now. https://github.com/odpf/firehose

파일 탐색기

최종 버전 다운로드 (.zip)

- - gradle.yml
- - checkstyle.xml
  - suppressions.xml
- - architecture.png
- test_messages.schema.json
- sample.properties
- - gradle-wrapper.jar
  - gradle-wrapper.properties
.gitignore
build.gradle
Dockerfile
FAQ.md
gradlew
gradlew.bat
LICENSE
README.md
run_descriptor_server.sh
settings.gradle

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/gojekfarm/beast

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd beast

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Docker

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Docker Desktop 컨테이너를 빌드하고 실행하려면 필요합니다. 설치 후 실행해서 백그라운드에 켜두세요.

docker run --env-file beast.env -v ./local_dir/project-secret.json:/var/bq-secret.json -it odpf/beast:$TAG

빌드된 이미지를 실제 컨테이너로 실행합니다.

✅ 터미널에 docker compose ps 를 입력해 컨테이너들이 Up 상태인지 확인하세요. README에 포트 번호가 적혀있다면 브라우저에서 http://localhost:포트번호 로 접속해보세요.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

3. Gradle (Java/Kotlin)

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
JDK (Java) Java/Kotlin 프로젝트를 빌드/실행하려면 필요합니다.
Gradle 레포에 포함된 gradlew(Gradle Wrapper)를 쓰면 Gradle을 따로 설치할 필요가 없습니다.

export $(cat ./env/sample.properties | xargs -L1) && gradle clean runConsumer

Gradle로 빌드를 진행합니다.

✅ BUILD SUCCESSFUL 메시지가 뜨면 성공입니다. build/ 폴더에 결과물이 생성됩니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# Beast is deprecated. Big Query sink is supported in [Firehose](https://github.com/odpf/firehose) now.

Beast is not supported. We recommend using Firehose for sinking Kafka data to BigQuery.

## Architecture

* **Consumer**:
    Consumes messages from kafka in batches, and pushes these batches to Read & Commit queues. These queues are blocking queues, i.e, no more messages will be consumed if the queue is full. (This is configurable based on poll timeout)
* **BigQuery Worker**:
    Polls messages from the read queue, and pushes them to BigQuery. If the push operation was successful, BQ worker sends an acknowledgement to the Committer.
* **Committer**:
    Committer receives the acknowledgements of successful push to BigQuery from BQ Workers. All these acknowledgements are stored in a set within the committer. Committer polls the commit queue for message batches. If that batch is present in the set, i.e., the batch has been successfully pushed to BQ, then it commits the max offset for that batch, back to Kafka, and pops it from the commit queue & set.

<br><div style="text-align:center;width: 90%; margin:auto;"><img src="docs/images/architecture.png" alt=""></div><br>

* **Dead Letters**:
    Beast provides a plugable GCS (Google Cloud Storage) component to store invalid out of bounds messages that are rejected by BigQuery. Primarily all messages that are partitioned on a timestamp field and those that contain out of ranges timestamps (year old data or 6 months in future) on the partition key are considered as invalid. Without an handler for these messages, Beast stops processing. The default behaviour is to stop processing on these out of range data. GCS component can be turned on by supplying an environment field as below.
    ```
    ENABLE_GCS_ERROR_SINK=true
    GCS_BUCKET=<google cloud store bucket name>
    GCS_PATH_PREFIX=<prefix path under the bucket>
    GCS_WRITER_PROJECT_NAME=<google project having bucket>
    ```
    The handler partitions the invalid messages on GCS based on the message arrival date in the format `<dt=yyyy-MM-dd>`. The location of invalid messages on GCS would ideally be `<GCS_WRITER_PROJECT_NAME>/<GCS_BUCKET>/<GCS_PATH_PREFIX>/<dt=yyyy-MM-dd>/<topicName>/<random-uuid>` where
    - `<topicName>` - is the topic that has the invalid messages
    - `<random-uuid>` - name of the file

## Building & Running

### Prerequisite
* A kafka cluster which has messages pushed in proto format, which beast can consume
* should have BigQuery project which has streaming permission
* create a table for the message proto
* create configuration with column mapping for the above table and configure in env file
* env file should be updated with bigquery, kafka, and application parameters

## Run locally:
```
git clone https://github.com/odpf/beast
export $(cat ./env/sample.properties | xargs -L1) && gradle clean runConsumer
```

## Run with Docker
The image is available in [odpf](https://hub.docker.com/r/odpf/beast) dockerhub.

```
export TAG=release-0.1.1
docker run --env-file beast.env -v ./local_dir/project-secret.json:/var/bq-secret.json -it odpf/beast:$TAG
```
* `-v` mounts local secret file `project-sercret.json` to the docker mentioned location, and `GOOGLE_CREDENTIALS` should match the same `/var/bq-secret.json` which is used for BQ authentication.
* `TAG`You could update the tag if you want the latest image, the mentioned tag is tested well.

## Running on Kubernetes

Create a beast deployment for a topic in kafka, which needs to be pushed to BigQuery.
* Deployment can have multiple instance of beast
* A beast container consists of the following threads:
  - A kafka consumer
  - Multiple BQ workers
  - A committer
* Deployment also includes telegraf container which pushes stats metrics
Follow the [instructions](https://github.com/gojektech/charts/tree/master/incubator/beast) in [chart](https://github.com/gojektech/charts) for helm deployment

## BQ Setup:
Given a [TestMessage](./src/test/proto/TestMessage.proto) proto file, you can create bigtable with [schema](./docs/test_messages.schema.json)
```
# create new table from schema
bq mk --table <project_name>:dataset_name.test_messages ./docs/test_messages.schema.json

# query total records
bq query --nouse_legacy_sql 'SELECT count(*) FROM `<project_name>:dataset_name.test_messages LIMIT 10'

#  update bq schema from local schema json file
bq update --format=prettyjson <project_name>:dataset_name.test_messages  booking.schema

# dump the schema of table to file
bq show --schema --format=prettyjson <project_name>:dataset_name.test_messages > test_messages.schema.json
```

## Produce messages to Kafka
You can generate messages with TestMessage.proto with [sample-kafka-producer](https://github.com/gojekfarm/sample-kafka-producer), which pushes N messages

## Running Stencil Server
* run shell script `./run_descriptor_server.sh` to build descriptor in `build` directory, and python server on `:8000`
* stencil url can be configured to `curl http://localhost:8000/messages.desc`

# Contribution

* You could raise issues or clarify the questions
* You could raise a PR for any feature/issues
* You could help us with documentation

To run and test locally:
```
git clone https://github.com/odpf/beast
export $(cat ./env/sample.properties | xargs -L1) && gradlew test
```