Scenario and Data

What do we show in this demo
- Flink SQL processing data from different storage systems
- Flink SQL using Hive Metastore as an external, persistent catalog
- Batch/Stream unification of queries in action
- Different ways to join dynamic data
- Creating Tables with DDL
Scenario is an online store receiving orders.
The order system consists of six tables (derived from the well-known TPC-H benchmark)
- orders: one row for each order
- lineitem: individual items of an order
- customer: customer data
- nation: denormalized nation data
- region: denormalized region data
- rates: exchange rates for currencies

Depending on their update characteristic (frequency, insert-only) tables are stored in different systems:

Kafka: orders, linetimes, rates
MySQL: customer, nation, region

Prepare the Data

Please download the TPCH demo data from Google Drive and extract the zip archive into the ./data folder (as ./data/*.tbl).

https://drive.google.com/file/d/15LWUBGZenWaW3R_WNxqvcTQOuA_Kgtjv

The folder will be mounted by the Docker containers.

Build the Docker Images

docker-compose build

Start the Docker Environment

docker-compose up -d

Show the Demo Environment

The demo environment consists of the following components.

Kafka + Data Provider (+ Zookeeper)

Show Kafka data streams

docker-compose exec kafka kafka-console-consumer.sh --bootstrap-server kafka:9092 --from-beginning --topic orders
docker-compose exec kafka kafka-console-consumer.sh --bootstrap-server kafka:9092 --from-beginning --topic lineitem
docker-compose exec kafka kafka-console-consumer.sh --bootstrap-server kafka:9092 --from-beginning --topic rates

MySQL

Show data in MySQL

docker-compose exec mysql mysql -Dsql-demo -usql-demo -pdemo-sql

SHOW TABLES;
DESCRIBE PROD_CUSTOMER;
SELECT * FROM PROD_CUSTOMER LIMIT 10;
quit;

Minio (S3-compatible Storage)

No files here yet
Show in Web UI: http://localhost:9000 (user: sql-demo, password: demo-sql)

Flink JobManager and TaskManager

Queries are executed on a FLink cluster
Show Web UI: http://localhost:8081

SQL client + Hive Metastore

SQL client provides SQL environment and submits queries to Flink for execution
Hive Metastore presistently stores catalog data

The Demo

Start the SQL Client

docker-compose exec sql-client ./sql-client.sh

Show Tables

All dynamic (Kafka-backed) tables are in the default catalog:

SHOW TABLES;
DESCRIBE prod_orders;
SELECT * FROM prod_orders;

All static (MySQL-backed) tables are in the Hive catalog:

USE CATALOG hive;
SHOW TABLES;
DESCRIBE prod_nation;
SELECT * FROM prod_nation;

Running Queries on Static and Dynamic Tables

Flink SQL unifies batch and stream processing.
Many queries can be executed on static and dynamic table.

Take a Snapshot of a Dynamic Table

Create a table backed by a file in S3.

CREATE TABLE dev_orders (
  o_orderkey      INTEGER,
  o_custkey       INTEGER,
  o_orderstatus   STRING,
  o_totalprice    DOUBLE,
  o_currency      STRING,
  o_ordertime     TIMESTAMP(3),
  o_orderpriority STRING,
  o_clerk         STRING, 
  o_shippriority  INTEGER,
  o_comment       STRING,
  WATERMARK FOR o_ordertime AS o_ordertime - INTERVAL '5' MINUTE
) WITH (
  'connector.type' = 'filesystem',
  'connector.path' = 's3://sql-demo/orders.tbl',
  'format.type' = 'csv',
  'format.field-delimiter' = '|'
);

Write a sample of a dynamic streaming table to the static table.

INSERT INTO dev_orders SELECT * FROM default_catalog.default_database.prod_orders;

Go to Flink UI and show number of processed records.
Manually cancel the job
Show file in Minio: http://localhost:9000
Show data in SQL client

SELECT * FROM dev_orders;
SELECT COUNT(*) AS rowCnt FROM dev_orders;

Run a Query on the Snapshotted Data

Using the batch engine for better efficieny

SET execution.type=batch;

Compute revenue per currency and minute

SELECT
  CEIL(o_ordertime TO MINUTE) AS `minute`,
  o_currency AS `currency`,
  SUM(o_totalprice) AS `revenue`,
  COUNT(*) AS `orderCnt`
FROM dev_orders
GROUP BY
  o_currency,
  CEIL(o_ordertime TO MINUTE);

Run the same in streaming

SET execution.type=streaming;

Show why we should streamify the query

SET execution.result-mode=changelog;

Reset to previous settings

SET execution.result-mode=table;
SET execution.type=batch;

We can streamify the query a bit with a TUMBLE window

SELECT
  TUMBLE_END(o_ordertime, INTERVAL '1' MINUTE) AS `minute`,
  o_currency AS `currency`,
  SUM(o_totalprice) AS `revenue`,
  COUNT(*) AS `orderCnt`
FROM dev_orders
GROUP BY
  o_currency,
  TUMBLE(o_ordertime, INTERVAL '1' MINUTE);

Query is still executed with the batch engine

Move query to streaming data

We can run the same query on the dynamic table

SET execution.type=streaming;

We only change the table in the FROM clause

SELECT
  TUMBLE_END(o_ordertime, INTERVAL '1' MINUTE) AS `minute`,
  o_currency AS `currency`,
  SUM(o_totalprice) AS `revenue`,
  COUNT(*) AS `orderCnt`
FROM default_catalog.default_database.prod_orders
GROUP BY
  o_currency,
  TUMBLE(o_ordertime, INTERVAL '1' MINUTE);

Five Ways to Join Tables

Dynamic tables can be joined just like static tables.
- However, joins can be inefficient, if you ignore the dynamic property of tables.
- There are common patterns to join that can be efficiently executed with low resource consumption.

Regular Join on Static Tables

SET execution.type=batch;

show customers and their orders by region and priority for a specific day
query joins the snapshot of the orders table

SELECT
  r_name AS `region`,
  o_orderpriority AS `priority`,
  COUNT(DISTINCT c_custkey) AS `number_of_customers`,
  COUNT(o_orderkey) AS `number_of_orders`
FROM dev_orders
JOIN prod_customer ON o_custkey = c_custkey
JOIN prod_nation ON c_nationkey = n_nationkey
JOIN prod_region ON n_regionkey = r_regionkey
WHERE
  FLOOR(o_ordertime TO DAY) = TIMESTAMP '2020-04-01 0:00:00.000'
  AND NOT o_orderpriority = '4-NOT SPECIFIED'
GROUP BY r_name, o_orderpriority
ORDER BY r_name, o_orderpriority;

Regular Join on Dynamic Tables

We can run the same query on the prod_orders table
Change dev_orders to prod_orders
Remove ORDER BY clause

SET execution.type=streaming;

SELECT
  r_name AS `region`,
  o_orderpriority AS `priority`,
  COUNT(DISTINCT c_custkey) AS `number_of_customers`,
  COUNT(o_orderkey) AS `number_of_orders`
FROM default_catalog.default_database.prod_orders
JOIN prod_customer ON o_custkey = c_custkey
JOIN prod_nation ON c_nationkey = n_nationkey
JOIN prod_region ON n_regionkey = r_regionkey
WHERE
  FLOOR(o_ordertime TO DAY) = TIMESTAMP '2020-04-01 0:00:00.000'
  AND NOT o_orderpriority = '4-NOT SPECIFIED'
GROUP BY r_name, o_orderpriority;

Notable properties of this join
- The batch tables are read just once. Updates are not considered!
- All input tables are completely materialized in state.

Interval Join

A common requirement is to join events of two (or more) dynamic tables that are related with each other in a temporal context, for example events that happened around the same time.
Flink SQL features special optimizations for such joins.
First switch to the default catalog (which contains all dynamic tables)

USE CATALOG default_catalog;

Join prod_lineitem and prod_orders to compute open lineitems with urgent order priority.

SELECT
  o_ordertime AS `ordertime`,
  o_orderkey AS `order`,
  l_linenumber AS `linenumber`,
  l_partkey AS `part`,
  l_suppkey AS `supplier`,
  l_quantity AS `quantity`
FROM prod_lineitem
JOIN prod_orders ON o_orderkey = l_orderkey
WHERE
  l_ordertime BETWEEN o_ordertime - INTERVAL '5' MINUTE AND o_ordertime AND
  l_linestatus = 'O' AND
  o_orderpriority = '1-URGENT';

Notable properties of the join
- Event-time semantics (processing time also supported)
- Small state requirements. Only the 5 minute tails of the streams need to be held in state.

Enrichment Join with Lookup Table in MySQL

Enriching an insert-only, dynamic table with data from a static or slowly changing table
Example query: enrich prod_lineitem table with current exchange rate from prod_rates table to compute EUR-normalize amounts.

SELECT
  l_proctime AS `querytime`,
  l_orderkey AS `order`,
  l_linenumber AS `linenumber`,
  l_currency AS `currency`,
  rs_rate AS `cur_rate`, 
  (l_extendedprice * (1 - l_discount) * (1 + l_tax)) / rs_rate AS `open_in_euro`
FROM prod_lineitem
JOIN hive.`default`.prod_rates FOR SYSTEM_TIME AS OF l_proctime ON rs_symbol = l_currency
WHERE
  l_linestatus = 'O';

Check PROD_RATES table in MySQL and update a rate

docker-compose exec mysql mysql -Dsql-demo -usql-demo -pdemo-sql
SELECT * FROM PROD_RATES;
UPDATE PROD_RATES SET RS_TIMESTAMP = '2020-04-01 01:00:00.000', RS_RATE = 1.234 WHERE RS_SYMBOL='USD';

Notable properties of the join
- Processing time semantics: rates are looked up from MySQL table as of the processing time of a row
- MySQL table can be updated while the job is running

Enrichment Join against Temporal Table

Same use case as before.
Instead of looking up rates from MySQL, we ingest updates from another Kafka table.
Kafka table is accessed via a TemporalTableFunction prod_rates_temporal which looks up the most-recent exchange rate for a currency.

SELECT
  l_ordertime AS `ordertime`,
  l_orderkey AS `order`,
  l_linenumber AS `linenumber`,
  l_currency AS `currency`,
  rs_rate AS `cur_rate`, 
  (l_extendedprice * (1 - l_discount) * (1 + l_tax)) / rs_rate AS `open_in_euro`
FROM
  prod_lineitem,
  LATERAL TABLE(prod_rates_temporal(l_ordertime))
WHERE rs_symbol = l_currency AND
  l_linestatus = 'O';

Notable properties of the join
- Event time semantics: rates are looked up from temporal table (backed by a Kafka topic) as of the order time of the item.
- Rates change as records are produced into Kafka topic

Matching Patterns on Dynamic Tables

Matching patterns on new data with low latency is a common use case for stream processing
SQL:2016 introduced the MATCH RECOGNIZE clause to match patterns on tables
The combination of MATCH RECOGNIZE and dynamic tables is very powerful
Flink supports MATCH RECOGNIZE since several releases
Find customers that have changed their delivery behavior.
- Search for a pattern where the last x lineitems had regular shippings
- But now the customer whats to pay on delivery (collect on delivery = CoD)

CREATE VIEW lineitem_with_customer AS
SELECT * FROM (
  SELECT
    o_custkey AS `custkey`,
    l_orderkey AS `orderkey`,
    l_linenumber AS `linenumber`,
    l_ordertime AS `ordertime`,
    l_shipinstruct AS `shipinstruct`
  FROM prod_lineitem
  JOIN prod_orders ON o_orderkey = l_orderkey
  WHERE
    l_ordertime BETWEEN o_ordertime - INTERVAL '5' MINUTE AND o_ordertime
);

SELECT *
FROM lineitem_with_customer
MATCH_RECOGNIZE(
  PARTITION BY custkey
  ORDER BY ordertime
  MEASURES
    COUNT(OTHER.orderkey) AS `last regular shipings`,
    LAST(OTHER.orderkey) AS `last regular order`,
    LAST(OTHER.linenumber) AS `last regular linenumber`,
    COD.orderkey AS `cod order`,
    COD.linenumber AS `cod linenumber`
  PATTERN (OTHER{5,} COD)
  DEFINE
    OTHER AS NOT shipinstruct = 'COLLECT COD',
    COD AS shipinstruct = 'COLLECT COD'
);

Maintaining Materialized Views

Continuous queries on dynamic tables update their result with low latency.
Results can be maintained in external systems similar to materialized views.

Writing Results to Kafka

Let's write some data back to Kafka
We use the window aggregation query that we've discussed before

USE CATALOG default_catalog;

SELECT
  TUMBLE_END(o_ordertime, INTERVAL '1' MINUTE) AS `minute`,
  o_currency AS `currency`,
  SUM(o_totalprice) AS `revenue`,
  COUNT(*) AS `orderCnt`
FROM default_catalog.default_database.prod_orders
GROUP BY
  o_currency,
  TUMBLE(o_ordertime, INTERVAL '1' MINUTE);

Create a Kafka-backed table minute_stats with JSON encoding

CREATE TABLE minute_stats (
  `minute` TIMESTAMP(3),
  `currency` STRING,
  `revenueSum` DOUBLE,
  `orderCnt` BIGINT,
  WATERMARK FOR `minute` AS `minute` - INTERVAL '10' SECOND
) WITH (
  'connector.type' = 'kafka',       
  'connector.version' = 'universal',
  'connector.topic' = 'minute_stats',
  'connector.properties.zookeeper.connect' = 'not-needed',
  'connector.properties.bootstrap.servers' = 'kafka:9092',
  'connector.startup-mode' = 'earliest-offset',
  'format.type' = 'json'
);

Automatic topic creation is enabled in Kafka
Write query result into table

INSERT INTO minute_stats
SELECT
  TUMBLE_END(o_ordertime, INTERVAL '1' MINUTE) AS `minute`,
  o_currency AS `currency`,
  SUM(o_totalprice) AS `revenue`,
  COUNT(*) AS `orderCnt`
FROM default_catalog.default_database.prod_orders
GROUP BY
  o_currency,
  TUMBLE(o_ordertime, INTERVAL '1' MINUTE);

Check results in Kafka

docker-compose exec kafka kafka-console-consumer.sh --bootstrap-server kafka:9092 --from-beginning --topic minute_stats

Writing Results to MySQL

We would like to store and mainain the result of the following query in MySQL.

USE CATALOG hive;

SELECT
  r_name AS `region`,
  HOUR(o_ordertime) AS `hour_of_day`,
  COUNT(DISTINCT c_custkey) AS `number_of_customers`,
  COUNT(o_orderkey) AS `number_of_orders`
FROM (
  SELECT CAST(o_ordertime AS TIMESTAMP) o_ordertime, o_orderkey, o_custkey
  FROM default_catalog.default_database.prod_orders)
JOIN prod_customer ON o_custkey = c_custkey
JOIN prod_nation ON c_nationkey = n_nationkey
JOIN prod_region ON n_regionkey = r_regionkey
GROUP BY 
  r_name, 
  HOUR(o_ordertime);

Create a backing table in MySQL

docker-compose exec mysql mysql -Dsql-demo -usql-demo -pdemo-sql

CREATE TABLE REGION_STATS (
  region VARCHAR(20) NOT NULL,
  hour_of_day BIGINT NOT NULL,
  number_of_customers BIGINT NOT NULL,
  number_of_orders BIGINT NOT NULL,
  PRIMARY KEY (region, hour_of_day));

Create a table in Flink

CREATE TABLE region_stats (
  region               STRING,
  hour_of_day          BIGINT,
  number_of_customers  BIGINT,
  number_of_orders     BIGINT
) WITH (
  'connector.type' = 'jdbc',
  'connector.url' = 'jdbc:mysql://mysql:3306/sql-demo',
  'connector.table' = 'REGION_STATS',
  'connector.driver' = 'com.mysql.jdbc.Driver',
  'connector.username' = 'sql-demo',
  'connector.password' = 'demo-sql',
  'connector.write.flush.interval' = '2s',
  'connector.write.flush.max-rows' = '1000'
);

The table is configured to emit updates every second (or every 1000 updates).
Write query result to MySQL

INSERT INTO region_stats
SELECT
  r_name AS `region`,
  HOUR(o_ordertime) AS `hour_of_day`,
  COUNT(DISTINCT c_custkey) AS `number_of_customers`,
  COUNT(o_orderkey) AS `number_of_orders`
FROM (
  SELECT CAST(o_ordertime AS TIMESTAMP) o_ordertime, o_orderkey, o_custkey
  FROM default_catalog.default_database.prod_orders)
JOIN prod_customer ON o_custkey = c_custkey
JOIN prod_nation ON c_nationkey = n_nationkey
JOIN prod_region ON n_regionkey = r_regionkey
GROUP BY 
  r_name, 
  HOUR(o_ordertime);

Monitor query result in MySQL

docker-compose exec mysql mysql -Dsql-demo -usql-demo -pdemo-sql

SELECT * FROM REGION_STATS ORDER BY hour_of_day, region;

Name		Name	Last commit message	Last commit date
Latest commit History 23 Commits
client-image		client-image
data-image		data-image
data		data
flink-image		flink-image
flinksql-init		flinksql-init
metastore-image		metastore-image
mysql-init		mysql-init
.gitignore		.gitignore
README.md		README.md
connecting-to-systems.md		connecting-to-systems.md
docker-compose.yml		docker-compose.yml

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Repository files navigation

Scenario and Data

Prepare the Data

Build the Docker Images

Start the Docker Environment

Show the Demo Environment

Kafka + Data Provider (+ Zookeeper)

MySQL

Minio (S3-compatible Storage)

Flink JobManager and TaskManager

SQL client + Hive Metastore

The Demo

Start the SQL Client

Show Tables

Running Queries on Static and Dynamic Tables

Take a Snapshot of a Dynamic Table

Run a Query on the Snapshotted Data

Move query to streaming data

Five Ways to Join Tables

Regular Join on Static Tables

Regular Join on Dynamic Tables

Interval Join

Enrichment Join with Lookup Table in MySQL

Enrichment Join against Temporal Table

Matching Patterns on Dynamic Tables

Maintaining Materialized Views

Writing Results to Kafka

Writing Results to MySQL

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BerserkGuts/flink-sql-demo

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Repository files navigation

Scenario and Data

Prepare the Data

Build the Docker Images

Start the Docker Environment

Show the Demo Environment

Kafka + Data Provider (+ Zookeeper)

MySQL

Minio (S3-compatible Storage)

Flink JobManager and TaskManager

SQL client + Hive Metastore

The Demo

Start the SQL Client

Show Tables

Running Queries on Static and Dynamic Tables

Take a Snapshot of a Dynamic Table

Run a Query on the Snapshotted Data

Move query to streaming data

Five Ways to Join Tables

Regular Join on Static Tables

Regular Join on Dynamic Tables

Interval Join

Enrichment Join with Lookup Table in MySQL

Enrichment Join against Temporal Table

Matching Patterns on Dynamic Tables

Maintaining Materialized Views

Writing Results to Kafka

Writing Results to MySQL

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