com.qiniu:stream-docker

QStreaming is a framework that simplifies writing and executing ETLs on top of Apache Spark

License

License

Categories

Categories

Docker Container Virtualization Tools
GroupId

GroupId

com.qiniu
ArtifactId

ArtifactId

stream-docker
Last Version

Last Version

0.1.0
Release Date

Release Date

Type

Type

jar
Description

Description

QStreaming is a framework that simplifies writing and executing ETLs on top of Apache Spark

Download stream-docker

How to add to project

<!-- https://jarcasting.com/artifacts/com.qiniu/stream-docker/ -->
<dependency>
    <groupId>com.qiniu</groupId>
    <artifactId>stream-docker</artifactId>
    <version>0.1.0</version>
</dependency>
// https://jarcasting.com/artifacts/com.qiniu/stream-docker/
implementation 'com.qiniu:stream-docker:0.1.0'
// https://jarcasting.com/artifacts/com.qiniu/stream-docker/
implementation ("com.qiniu:stream-docker:0.1.0")
'com.qiniu:stream-docker:jar:0.1.0'
<dependency org="com.qiniu" name="stream-docker" rev="0.1.0">
  <artifact name="stream-docker" type="jar" />
</dependency>
@Grapes(
@Grab(group='com.qiniu', module='stream-docker', version='0.1.0')
)
libraryDependencies += "com.qiniu" % "stream-docker" % "0.1.0"
[com.qiniu/stream-docker "0.1.0"]

Dependencies

There are no dependencies for this project. It is a standalone project that does not depend on any other jars.

Project Modules

There are no modules declared in this project.

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Introduction

QStreaming is a framework that simplifies writing and executing ETLs on top of Apache Spark

It is based on a simple sql-like configuration file and runs on any Spark cluster

Getting started

Configurations

To run QStreaming you must first define Pipeline DSL file as below.

Pipeline DSL

For example a simple pipeline dsl file should be as follows:

-- DDL for streaming input which connect to a kafka topic
-- this declares five fields based on the JSON data format.In addition, it use the ROWTIME() to declare a virtual column that generate the event time attribute from existing ts field
create stream input table user_behavior(
  user_id LONG,
  item_id LONG,
  category_id LONG,
  behavior STRING,
  ts TIMESTAMP,
  eventTime as ROWTIME(ts,'1 minutes')
) using kafka(
  kafka.bootstrap.servers="localhost:localhost:9091",
  startingOffsets=earliest,
  subscribe="user_behavior",
  "group-id"="user_behavior"
);

-- DDL for streaming output which connect to a kafka topic
create stream output table behavior_cnt_per_hour
using kafka(
  kafka.bootstrap.servers="localhost:9091",
  topic="behavior_cnt_per_hour"
)TBLPROPERTIES(
  "update-mode"="update",
  checkpointLocation = "behavior_cnt_per_hour"
);

-- CREATE VIEW count the number of "buy" records in each hour window.
create view v_behavior_cnt_per_hour as
SELECT
   window(eventTime, "1 minutes") as window,
   COUNT(*) as behavior_cnt,
   behavior
FROM user_behavior
GROUP BY
  window(eventTime, "1 minutes"),
  behavior;

--  persist metric to kafka
insert into behavior_cnt_per_hour
select
   from_unixtime(cast(window.start as LONG)/1000,'yyyy-MM-dd HH:mm') as time,
   behavior_cnt,
   behavior
from
  v_behavior_cnt_per_hour;

Run QStreaming

There are three options to run QStreaming, first to get the latest released JAR from here

Run on a yarn cluster

To run on a cluster requires Apache Spark v2.2+

  • Run the following command:
$SPARK_HOME/bin/spark-submit
--class com.qiniu.stream.core.Streaming \
--master yarn \
--deploy-mode client \
stream-standalone-0.1.0-jar-with-dependencies.jar  \
-j pathToYourPipeline.dsl 
Run on a standalone cluster

To run on a standalone cluster you must first start a spark standalone cluster , and then run the following command:

$SPARK_HOME/bin/spark-submit
--class com.qiniu.stream.core.Streaming \
--master spark://IP:PORT \
stream-standalone-0.1.0-jar-with-dependencies.jar \
-j pathToYourPipeline.dsl 
Run as a library

It's also possible to use QStreaming inside your own project

To use it adds the dependency to your project

  • maven

    <dependency>
      <groupId>com.qiniu</groupId>
      <dependency>stream-core</dependency>
      <version>0.1.0</version>
    </dependency>
    
  • gradle

    compile 'com.qiniu:stream-core:0.1.0'
    
  • sbt

    libraryDependencies += "com.qiniu" % "stream-core" % "0.1.0"

Datasources

Features

DDL enhancement

QStreaming allow to connect to a stream source with DDL statement.

For example below define an input which connect to a kafka topic

create stream input table user_behavior(
  user_id LONG,
  item_id LONG,
  category_id LONG,
  behavior STRING,
  ts TIMESTAMP,
  eventTime as ROWTIME(ts,'1 minutes')
) using kafka(
  kafka.bootstrap.servers="localhost:9091",
  startingOffsets=earliest,
  subscribe="user_behavior",
  "group-id"="user_behavior"
);

Please refer to CreateSourceTableStatement and CreateSinkTableStatement for the detail of DDL statement .

Watermark support

QStreaming supports watermark which helps a stream processing engine to deal with late data.

There are two ways to use watermark for a stream processing engine

  • Adding ROWTIME(eventTimeField,delayThreshold) as a schema property in a ddl statement

    create stream input table user_behavior(
      user_id LONG,
      item_id LONG,
      category_id LONG,
      behavior STRING,
      ts TIMESTAMP,
      eventTime as ROWTIME(ts,'1 minutes')
    ) using kafka(
      kafka.bootstrap.servers="localhost:9091",
      startingOffsets=earliest,
      subscribe="user_behavior",
      "group-id"="user_behavior"
    );

    Above example means use eventTime as event time field with 5 minutes delay thresholds

  • Adding waterMark("eventTimeField, delayThreshold") as a view property in a view statement

    create view v_behavior_cnt_per_hour(waterMark = "eventTime, 1 minutes") as
    SELECT
       window(eventTime, "1 minutes") as window,
       COUNT(*) as behavior_cnt,
       behavior
    FROM user_behavior
    GROUP BY
      window(eventTime, "1 minutes"),
      behavior;

Above example define a watermark use eventTime field with 1 minute threshold

Dynamic user define function

-- define UDF named hello
create function hello(name:String) = {
   s"hello ${name}"
};

QStreaming allow to define a dynamic UDF inside job.dsl, for more detail information please refer to createFunctionStatement

Above example define UDF with a string parameter.

Multiple sink

QStreaming allow you to define multiple output for streaming process by leavarage foreEachBatch mode (only avaliable in spark>=2.4.0)

Below example will sink the behavior count metric to two kafka topics

    create stream output table output using kafka(
        kafka.bootstrap.servers=<kafkaBootStrapServers>,
        topic="topic1" 
    ),kafka(
        kafka.bootstrap.servers=<kafkaBootStrapServers>,
        topic="topic2" 
    ) TBLPROPERTIES (outputMode = update,checkpointLocation = "behavior_output");

For more information about how to create multiple sink please refer to createSinkTableStatement

Variable interpolation

QStreaming support variable interpolation from command line arguments , this is useful for running QStreaming as a periodic job, and referece them in sql file .

For example, you can pass the value for theDayThatRunAJob and theHourThatRunAJob from an Airflow DAG

$SPARK_HOME/bin/spark-submit
--name yourAppName \
--class com.qiniu.stream.core.Streaming \
stream-standalone-0.1.0-jar-with-dependencies.jar \
-j pathToYourPipeline.dsl \
-v day=theDayThatRunAJob \
-v hour=theHourThatRunAJob

and the pipeline dsl file

create batch input table raw_log
USING parquet(path="hdfs://cluster1/logs/day=${day}/hour=${hourt");

Monitor

Kafka lag monitor

QStreaming allow to monitor the kafka topic offset lag by adding the "group-id" connector property in ddl statement as below

create stream input table user_behavior(
  user_id LONG,
  item_id LONG,
  category_id LONG,
  behavior STRING,
  ts TIMESTAMP,
  eventTime as ROWTIME(ts,'1 minutes')
) using kafka(
  kafka.bootstrap.servers="localhost:9091",
  startingOffsets=earliest,
  subscribe="user_behavior",
  "group-id"="user_behavior"
);

Data Quality Check

The purpose is to "unit-test" data to find errors early, before the data gets fed to any storage.

For example, we test for the following properties of data :

  • there are 5 rows in total
  • values of the id attribute are never NULL and unique
  • values of the productName attribute are never NULL
  • the priority attribute can only contain "high" or "low" as value
  • numViews should not contain negative values
  • at least half of the values in description should contain a url
  • the median of numViews should be less than or equal to 10

In DSL this looks as follows:

CREATE TEST testName(testLevel=Error,testOutput=testResult) on dataset WITH 
   numRows()=5 and 
   isNotNull(id) and 
   isUnique(id) and 
   isComplete(productName) and 
   isContainedIn(priority, ["high", "low"]) and 
   isNonNegative(numViews)  and 
   containsUrl(description) >= 0.5 and 
   hasApproxQuantile(numViews, 0.5) <= 10

Architecture

Blogs

License

See the LICENSE file for license rights and limitations (Apache License).

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Versions

Version
0.1.0
0.0.4
0.0.3
0.0.2