Sylph

The Sylph is Streaming Job Manager.

Sylph uses SQL Query to describe calculations and bind multiple source(input)/sink(output) to visually develop and deploy streaming applications. Through Web IDE makes it easy to develop, deploy, monitor streaming applications and analyze streaming application behavior at any time.
Sylph has rich source/sink support and flexible extensions to visually develop and deploy stream analysis applications and visualized streaming application lifecycle management.

The Sylph core is to build distributed applications through workflow descriptions. Support for

Spark-Streaming (Spark1.x)
Structured-Streaming (Spark2.x)
Flink Streaming

License

Copyright (C) 2018 The Sylph Authors

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

StreamingSql

create function get_json_object as 'ideal.sylph.runner.flink.udf.UDFJson';

create source table topic1(
    _topic varchar,
    _key varchar,
    _partition integer,
    _offset bigint,
    _message varchar
) with (
    type = 'kafka08',
    kafka_topic = 'event_topic',
    auto.offset.reset = latest,
    kafka_broker = 'localhost:9092',
    kafka_group_id = 'test1',
    zookeeper.connect = 'localhost:2181'
);

-- 定义数据流输出位置
create sink table event_log(
    key varchar,
    user_id varchar,
    offset bigint
) with (
    type = 'kudu',
    kudu.hosts = 'localhost:7051',
    kudu.tableName = 'impala::test_kudu.log_events',
    kudu.mode = 'INSERT',
    batchSize = 5000
);

insert into event_log
select _key,get_json_object(_message, 'user_id') as user_id,_offset 
from topic1

UDF UDAF UDTF

The registration of the custom function is consistent with the hive

create function get_json_object as 'ideal.sylph.runner.flink.udf.UDFJson';

StreamETL

Support flink-stream spark-streaming spark-structured-streaming(spark2.2x)

Building

sylph builds use Gradle and requires Java 8.

# Build and install distributions
./gradlew clean assemble

Running Sylph in your IDE

After building Sylph for the first time, you can load the project into your IDE and run the server. Me recommend using IntelliJ IDEA.

After opening the project in IntelliJ, double check that the Java SDK is properly configured for the project:

Open the File menu and select Project Structure
In the SDKs section, ensure that a 1.8 JDK is selected (create one if none exist)
In the Project section, ensure the Project language level is set to 8.0 as Sylph makes use of several Java 8 language features
HADOOP_HOME(2.6.x+) SPARK_HOME(2.4.x+) FLINK_HOME(1.7.x+)

Sylph comes with sample configuration that should work out-of-the-box for development. Use the following options to create a run configuration:

Main Class: ideal.sylph.main.SylphMaster
VM Options: -Dconfig=etc/sylph/sylph.properties -Dlog4j.file=etc/sylph/sylph-log4j.properties
ENV Options: FLINK_HOME= HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
Working directory: sylph-dist/build
Use classpath of module: sylph-main

Useful mailing lists

yezhixinghai@gmail.com - For discussions about code, design and features
lydata_jia@163.com - For discussions about code, design and features
jeific@outlook.com - For discussions about code, design and features

Getting Help

Send message to Google Group
Add QQ Group: 438625067

nimuyuhan/sylph