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Version: 0.10.0

Flink Guide

This guide provides an instruction for Flink Hudi integration. We can feel the unique charm of how Flink brings in the power of streaming into Hudi. Reading this guide, you can quickly start using Flink on Hudi, learn different modes for reading/writing Hudi by Flink:

Quick Start​

Setup​

We use the Flink Sql Client because it's a good quick start tool for SQL users.

Hudi works with Flink-1.13.x version. You can follow instructions here for setting up Flink. The hudi-flink-bundle jar is archived with scala 2.11, so it’s recommended to use flink 1.13.x bundled with scala 2.11.

Start a standalone Flink cluster within hadoop environment. Before you start up the cluster, we suggest to config the cluster as follows:

Now starts the cluster:

# HADOOP_HOME is your hadoop root directory after unpack the binary package.
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`

# Start the Flink standalone cluster
./bin/start-cluster.sh

Hudi has a prepared bundle jar for Flink, which should be loaded in the Flink SQL Client when it starts up. You can build the jar manually under path hudi-source-dir/packaging/hudi-flink-bundle, or download it from the Apache Official Repository.

Now starts the SQL CLI:

# HADOOP_HOME is your hadoop root directory after unpack the binary package.
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`

./bin/sql-client.sh embedded -j .../hudi-flink-bundle_2.1?-*.*.*.jar shell

Please note the following:

  • We suggest hadoop 2.9.x+ version because some of the object storage has filesystem implementation only after that
  • The flink-parquet and flink-avro formats are already packaged into the hudi-flink-bundle jar

Setup table name, base path and operate using SQL for this guide. The SQL CLI only executes the SQL line by line.

Insert Data​

Creates a Flink Hudi table first and insert data into the Hudi table using SQL VALUES as below.

-- sets up the result mode to tableau to show the results directly in the CLI
set execution.result-mode=tableau;

CREATE TABLE t1(
  uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,
  name VARCHAR(10),
  age INT,
  ts TIMESTAMP(3),
  `partition` VARCHAR(20)
)
PARTITIONED BY (`partition`)
WITH (
  'connector' = 'hudi',
  'path' = '${path}',
  'table.type' = 'MERGE_ON_READ' -- this creates a MERGE_ON_READ table, by default is COPY_ON_WRITE
);

-- insert data using values
INSERT INTO t1 VALUES
  ('id1','Danny',23,TIMESTAMP '1970-01-01 00:00:01','par1'),
  ('id2','Stephen',33,TIMESTAMP '1970-01-01 00:00:02','par1'),
  ('id3','Julian',53,TIMESTAMP '1970-01-01 00:00:03','par2'),
  ('id4','Fabian',31,TIMESTAMP '1970-01-01 00:00:04','par2'),
  ('id5','Sophia',18,TIMESTAMP '1970-01-01 00:00:05','par3'),
  ('id6','Emma',20,TIMESTAMP '1970-01-01 00:00:06','par3'),
  ('id7','Bob',44,TIMESTAMP '1970-01-01 00:00:07','par4'),
  ('id8','Han',56,TIMESTAMP '1970-01-01 00:00:08','par4');

Query Data​

-- query from the Hudi table
select * from t1;

This query provides snapshot querying of the ingested data. Refer to Table types and queries for more info on all table types and query types supported.

Update Data​

This is similar to inserting new data.

-- this would update the record with key 'id1'
insert into t1 values
  ('id1','Danny',27,TIMESTAMP '1970-01-01 00:00:01','par1');

Notice that the save mode is now Append. In general, always use append mode unless you are trying to create the table for the first time. Querying the data again will now show updated records. Each write operation generates a new commit denoted by the timestamp. Look for changes in _hoodie_commit_time, age fields for the same _hoodie_record_keys in previous commit.

Streaming Query​

Hudi Flink also provides capability to obtain a stream of records that changed since given commit timestamp. This can be achieved using Hudi's streaming querying and providing a start time from which changes need to be streamed. We do not need to specify endTime, if we want all changes after the given commit (as is the common case).

CREATE TABLE t1(
  uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,
  name VARCHAR(10),
  age INT,
  ts TIMESTAMP(3),
  `partition` VARCHAR(20)
)
PARTITIONED BY (`partition`)
WITH (
  'connector' = 'hudi',
  'path' = '${path}',
  'table.type' = 'MERGE_ON_READ',
  'read.streaming.enabled' = 'true',  -- this option enable the streaming read
  'read.start-commit' = '20210316134557' -- specifies the start commit instant time
  'read.streaming.check-interval' = '4' -- specifies the check interval for finding new source commits, default 60s.
);

-- Then query the table in stream mode
select * from t1;

This will give all changes that happened after the read.streaming.start-commit commit. The unique thing about this feature is that it now lets you author streaming pipelines on streaming or batch data source.

Delete Data​

When consuming data in streaming query, Hudi Flink source can also accepts the change logs from the underneath data source, it can then applies the UPDATE and DELETE by per-row level. You can then sync a NEAR-REAL-TIME snapshot on Hudi for all kinds of RDBMS.

Where To Go From Here?​

Check out the Flink Setup how-to page for deeper dive into configuration settings.

If you are relatively new to Apache Hudi, it is important to be familiar with a few core concepts:

See more in the "Concepts" section of the docs.

Take a look at recent blog posts that go in depth on certain topics or use cases.

Hudi tables can be queried from query engines like Hive, Spark, Flink, Presto and much more. We have put together a demo video that show cases all of this on a docker based setup with all dependent systems running locally. We recommend you replicate the same setup and run the demo yourself, by following steps here to get a taste for it. Also, if you are looking for ways to migrate your existing data to Hudi, refer to migration guide.