MarkSfik commented on a change in pull request #335:
URL: https://github.com/apache/flink-web/pull/335#discussion_r420030866
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File path:
_posts/2020-05-03-flink-sql-demo-building-e2e-streaming-application.md
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+---
+layout: post
+title: "Flink SQL Demo: Building an End-to-End Streaming Application"
+date: 2020-05-03T12:00:00.000Z
+categories: news
+authors:
+- jark:
+ name: "Jark Wu"
+ twitter: "JarkWu"
+excerpt: Apache Flink 1.10 has released many exciting new features, including
many developments in Flink SQL which is evolving at a fast pace. This article
takes a closer look at how to quickly build streaming applications with Flink
SQL from a practical point of view.
+---
+
+Apache Flink 1.10.0 has released many exciting new features, including many
developments in Flink SQL which is evolving at a fast pace. This article takes
a closer look at how to quickly build streaming applications with Flink SQL
from a practical point of view.
+
+In the following sections, we describe how to integrate Kafka, MySQL,
Elasticsearch, and Kibana with Flink SQL to analyze ecommerce user behavior in
real-time. All exercises in this article are performed in the Flink SQL CLI,
while the entire process uses plain SQL text, without a single line of Java or
Scala code or IDE installation. The final result of this demo is shown in the
following figure:
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-03-flink-sql-demo/image1.png"
width="800px" alt="Demo Overview"/>
+</center>
+<br>
+
+# Preparation
+
+Prepare a Linux or MacOS computer with Docker and Java 8 installed. A Java
environment is required because we will install and run Flink cluster in the
host environment, not in a Docker container.
+
+## Use Docker Compose to Start Demo Environment
+
+The components required in this demo (except for Flink) are all managed in
containers, so we will use `docker-compose` to start them. First, download the
`docker-compose.yml` file that defines the demo environment, for example by
running the following commands:
+
+```
+mkdir flink-demo; cd flink-demo;
+wget
https://raw.githubusercontent.com/wuchong/flink-sql-demo/master/docker-compose.yml
+```
+
+The Docker Compose environment consists of the following containers:
+
+- **MySQL:** MySQL 5.7 and a `category` table in the database. The `category`
table will be joined with data in Kafka to enrich the real-time data.
+- **Kafka:** It is mainly used as a data source. The DataGen component
automatically writes data into a Kafka topic.
+- **Zookeeper:** This component is required by Kafka.
+- **Elasticsearch:** It is mainly used as a data sink.
+- **Kibana:** It's used to visualize the data in Elasticsearch.
+- **DataGen:** It is the data generator. After the container is started, user
behavior data is automatically generated and sent to the Kafka topic. By
default, 2000 data entries are generated each second for about 1.5 hours. You
can modify datagen's `speedup` parameter in `docker-compose.yml` to adjust the
generation rate (which takes effect after docker compose is restarted).
+
+**Important:** Before starting the containers, we recommend configuring Docker
so that sufficient resources are available and the environment does not become
unresponsive. We suggest running Docker at 3-4 GB memory and 3-4 CPU cores.
+
+To start all containers, run the following command in the directory that
contains the `docker-compose.yml` file.
+
+```
+docker-compose up -d
+```
+
+This command automatically starts all the containers defined in the Docker
Compose configuration in a detached mode. Run `docker ps` to check whether the
five containers are running properly. You can also visit
[http://localhost:5601/](http://localhost:5601/) to see if Kibana is running
normally.
+
+Don’t forget to run the following command to stop all containers after you
finished the tutorial:
+
+```
+docker-compose down
+```
+
+## Download and Install Flink Cluster
+
+We recommend to manually download and install Flink on your host system,
instead of starting Flink through Docker because you’ll get a more intuitive
understanding of the components, dependencies, and scripts of Flink.
+
+1. Download and decompress [Apache Flink
1.10.0](https://www.apache.org/dist/flink/flink-1.10.0/flink-1.10.0-bin-scala_2.11.tgz)
into the `flink-1.10.0` directory:
+2. Go to the `flink-1.10.0` directory by running `cd flink-1.10.0`.
+3. Run the following command to download the JAR dependency package and copy
it to the `lib/` directory.
+
+ ```
+wget -P ./lib/
https://repo1.maven.org/maven2/org/apache/flink/flink-json/1.10.0/flink-json-1.10.0.jar
| \
+ wget -P ./lib/
https://repo1.maven.org/maven2/org/apache/flink/flink-sql-connector-kafka_2.11/1.10.0/flink-sql-connector-kafka_2.11-1.10.0.jar
| \
+ wget -P ./lib/
https://repo1.maven.org/maven2/org/apache/flink/flink-sql-connector-elasticsearch6_2.11/1.10.0/flink-sql-connector-elasticsearch6_2.11-1.10.0.jar
| \
+ wget -P ./lib/
https://repo1.maven.org/maven2/org/apache/flink/flink-jdbc_2.11/1.10.0/flink-jdbc_2.11-1.10.0.jar
| \
+ wget -P ./lib/
https://repo1.maven.org/maven2/mysql/mysql-connector-java/5.1.48/mysql-connector-java-5.1.48.jar
+```
+
+4. In `conf/flink-conf.yaml`, set `taskmanager.numberOfTaskSlots` to `10`,
since during this demo we will be launching multiple jobs.
+5. Run `./bin/start-cluster.sh` to start the cluster. Check if Flink is up by
accessing the Flink Web UI at [http://localhost:8081](http://localhost:8081).
The number of available slots should be 10.
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-03-flink-sql-demo/image2.png"
width="800px" alt="Demo Overview"/>
+</center>
+<br>
+6. Run `bin/sql-client.sh embedded` to start the SQL CLI. You will see the
following squirrel welcome page.
+
+<center>
+<img src="{{ site.baseurl }}/img/blog/2020-05-03-flink-sql-demo/image3.png"
width="800px" alt="Flink SQL CLI welcome page"/>
+</center>
+<br>
+
+# Create a Kafka table using DDL
+
+The Datagen container continuously writes events into the Kafka
`user_behavior` topic. This data contains the user behavior on the day of
November 27, 2017 (behaviors include “click”, “like”, “purchase” and “add to
shopping cart” events). Each row represents a user behavior event, with the
user ID, product ID, product category ID, event type, and timestamp in JSON
format. Note that the dataset is from the [Alibaba Cloud Tianchi public
dataset](https://tianchi.aliyun.com/dataset/dataDetail?dataId=649).
+
+In the directory that contains `docker-compose.yml`, run the following command
to view the first 10 data entries generated in the Kafka topic:
+
+```
+docker-compose exec kafka bash -c 'kafka-console-consumer.sh --topic
user_behavior --bootstrap-server kafka:9094 --from-beginning --max-messages 10'
+
+{"user_id": "952483", "item_id":"310884", "category_id": "4580532",
"behavior": "pv", "ts": "2017-11-27T00:00:00Z"}
+{"user_id": "794777", "item_id":"5119439", "category_id": "982926",
"behavior": "pv", "ts": "2017-11-27T00:00:00Z"}
+...
+```
+
+In order to make the events in the Kafka topic accessible to Flink SQL, we run
the following DDL statement to create a table that connects to the topic in the
Kafka cluster:
+
+```sql
+CREATE TABLE user_behavior (
+ user_id BIGINT,
+ item_id BIGINT,
+ category_id BIGINT,
+ behavior STRING,
+ ts TIMESTAMP(3),
+ proctime AS PROCTIME(), -- generates processing-time attribute using
computed column
+ WATERMARK FOR ts AS ts - INTERVAL '5' SECOND -- defines watermark on ts
column, marks ts as event-time attribute
+) WITH (
+ 'connector.type' = 'kafka', -- using kafka connector
+ 'connector.version' = 'universal', -- kafka version, universal supports
Kafka 0.11+
+ 'connector.topic' = 'user_behavior', -- kafka topic
+ 'connector.startup-mode' = 'earliest-offset', -- reading from the
beginning
+ 'connector.properties.zookeeper.connect' = 'localhost:2181', -- zookeeper
address
+ 'connector.properties.bootstrap.servers' = 'localhost:9092', -- kafka
broker address
+ 'format.type' = 'json' -- the data format is json
+);
+```
+
+The above snippet declares five fields based on the data format. In addition,
it uses the computed column syntax and built-in `PROCTIME()` function to
declare a virtual column that generates the processing-time attribute. It also
uses the WATERMARK syntax to declare the watermark strategy on the `ts` field
(tolerate 5-seconds out-of-order). Therefore, the `ts` field becomes an
event-time attribute. For more information about time attributes and DDL
syntax, see the following official documents:
+
+- [For time
attributes](https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/table/streaming/time_attributes.html)
+- [For SQL
DDL](https://ci.apache.org/projects/flink/flink-docs-release-1.10/dev/table/sql/create.html#create-table)
+
+After creating the `user_behavior` table in the SQL CLI, run `show tables;`
and `describe user_behavior;` to see registered tables and table details. Also,
run the command `SELECT * FROM user_behavior;` directly in the SQL CLI to
preview the data (press `q` to exit).
+
+Next, we discover more about Flink SQL through three real scenarios.
+
+# Hourly Trading Volume
+
+## Create Elasticsearch table using DDL
+
+Let’s create an Elasticsearch result table in the SQL CLI. We need two columns
in this case: `hour_of_day` and `buy_cnt` (trading volume).
+
+```sql
+CREATE TABLE buy_cnt_per_hour (
+ hour_of_day BIGINT,
+ buy_cnt BIGINT
+) WITH (
+ 'connector.type' = 'elasticsearch', -- using elasticsearch connector
+ 'connector.version' = '6', -- elasticsearch version, 6 supports both 6+
and 7+
+ 'connector.hosts' = 'http://localhost:9200', -- elasticsearch address
+ 'connector.index' = 'buy_cnt_per_hour', -- elasticsearch index name,
similar to database table name
+ 'connector.document-type' = 'user_behavior', -- elasticsearch type name
+ 'connector.bulk-flush.max-actions' = '1', -- refresh for every row
+ 'format.type' = 'json', -- output data in json format
+ 'update-mode' = 'append'
+);
+```
+
+There is no need to create the `buy_cnt_per_hour` index in Elasticsearch in
advance since Elasticsearch will automatically create the index if it doesn’t
exist.
Review comment:
```suggestion
There is no need to create the `buy_cnt_per_hour` index in Elasticsearch, in
advance since Elasticsearch will automatically create the index if it does not
exist.
```
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