#stream #window #flink

rlink

High performance Stream Processing Framework

64 releases (5 breaking)

0.6.16 Aug 31, 2021
0.5.12 Aug 4, 2021
0.5.5 Jul 30, 2021
0.2.5 Mar 29, 2021
0.1.1 Dec 26, 2020

#60 in Visualization

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77 downloads per month
Used in fewer than 10 crates

MIT/Apache

595KB
15K SLoC

rlink-rs

Crates.io Released API docs MIT licensed License

High performance Stream Processing Framework. A new, faster, implementation of Apache Flink from scratch in Rust. pure memory, zero copy. single cluster in the production environment stable hundreds of millions per second window calculation.

Framework tested on Linux/MacOS/Windows, requires stable Rust.

Monitor

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Graph

Graph Evolution

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Example

rlink = "0.6"
SELECT
  HOP_START(timestamp, INTERVAL '20' SECOND, INTERVAL '60' SECOND),
  HOP_END(timestamp, INTERVAL '20' SECOND, INTERVAL '60' SECOND),
  name,
  SUM(value),
  MAX(value),
  MIN(value),
  COUNT(*),
FROM stream_table
GROUP BY HOP(timestamp, INTERVAL '20' SECOND, INTERVAL '60' SECOND), name
#[derive(Clone, Debug)]
pub struct SimpleStreamApp {}

impl StreamApp for SimpleStreamApp {
    fn prepare_properties(&self, properties: &mut Properties) {
        properties.set_application_name("rlink-simple");
    }

    fn build_stream(&self, _properties: &Properties, env: &mut StreamExecutionEnvironment) {
        env.register_source(vec_source(gen_records(), &model::FIELD_METADATA), 1)
            .assign_timestamps_and_watermarks(
                DefaultWatermarkStrategy::new()
                    .for_bounded_out_of_orderness(Duration::from_secs(1))
                    .wrap_time_periodic(Duration::from_secs(10), Duration::from_secs(20))
                    .for_schema_timestamp_assigner("timestamp"),
            )
            .key_by(SchemaKeySelector::new(vec!["name"]))
            .window(SlidingEventTimeWindows::new(
                Duration::from_secs(60),
                Duration::from_secs(20),
                None,
            ))
            .reduce(
                SchemaReduceFunction::new(vec![sum("value"), max("value"), min("value"), count()]),
                2,
            )
            .add_sink(print_sink());
    }
}

Build

Build source

# debug
cargo build --color=always --all --all-targets
# release
cargo build --release --color=always --all --all-targets

Standalone Deploy

Config

standalone.yaml


---
# all job manager's addresses, one or more
application_manager_address:
  - "http://0.0.0.0:8770"
  - "http://0.0.0.0:8770"

metadata_storage:
  type: Memory

# bind ip
task_manager_bind_ip: 0.0.0.0
task_manager_work_dir: /data/rlink/application

task_managers

TaskManager list

10.1.2.1
10.1.2.2
10.1.2.3
10.1.2.4

Launch

Coordinator

./start_job_manager.sh

Worker

./start_task_manager.sh

Submit Application

On Standalone

## submit an application

# create job
curl http://x.x.x.x:8770/job/application \
  -X POST \
  -F "file=@/path/to/execute_file" \
  -v

# run job
curl http://x.x.x.x:8770/job/application/application-1591174445599 \
  -X POST \
  -H "Content-Type:application/json" \
  -d '{"batch_args":[{"cluster_mode":"Standalone", "manager_type":"Coordinator","num_task_managers":"15"}]}' \
  -v

# kill job
curl http://x.x.x.x:8770/job/application/application-1591174445599/shutdown \
  -X POST \
  -H "Content-Type:application/json"

On Yarn

update manager jar to hdfs

upload rlink-yarn-manager-{version}-jar-with-dependencies.jar to hdfs

eg: upload to hdfs://nn/path/to/rlink-yarn-manager-{version}-jar-with-dependencies.jar

update dashboard to hdfs

upload rlink-dashboard.zip to hdfs

eg: upload to hdfs://nn/path/to/rlink-dashboard.zip

update application to hdfs

upload your application executable file to hdfs.

eg: upload rlink-showcase to hdfs://nn/path/to/rlink-showcase

submit yarn job

submit yarn job with rlink-yarn-client-{version}.jar

hadoop jar rlink-yarn-client-{version}.jar rlink.yarn.client.Client \
  --applicationName rlink-showcase \
  --worker_process_path hdfs://nn/path/to/rlink-showcase \
  --java_manager_path hdfs://nn/path/to/rlink-yarn-manager-{version}-jar-with-dependencies.jar \
  --yarn_manager_main_class rlink.yarn.manager.ResourceManagerCli \
  --dashboard_path hdfs://nn/path/to/rlink-dashboard.zip \
  --master_memory_mb 256 \
  --master_v_cores 1 \
  --memory_mb 256 \
  --v_cores 1 \
  --queue root.default \
  --cluster_mode YARN \
  --manager_type Coordinator \
  --num_task_managers 80 \
  --application_process_arg xxx

On Kubernetes

Preparation

  • Kubernetes
  • KubeConfig, configurable via ~/.kube/config. You can verify permissions by running kubectl auth can-i <list|create|edit|delete> pods

take a look at how to setup a Kubernetes cluster.

# start 
./target/release/rlink-kubernetes \
  name=my_first_rlink_application \
  image_path=name:tag \
  job_v_cores=1 \
  job_memory_mb=100 \
  task_v_cores=1 \
  task_memory_mb=100 \
  num_task_managers=1  \

# stop
kubectl delete deployment/my_first_rlink_application

Build image example-simple

sudo docker build -t xxx:xx -f ./docker/Dockerfile_example_simple .

Dependencies

~23–45MB
~836K SLoC