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[Demo 3] Sync Data from MySQL to Hive Using SeaTunnel

Apache SeaTunnel
6 min read4 days ago

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We are launching a Demo demonstration plan in the Apache SeaTunnel community to showcase how to use connectors. We invite all data synchronization enthusiasts to share their knowledge and practical experience!

Our third session is themed on How to synchronize from MySQL to Hive using SeaTunnel.

Highlight! Highlight! If you’re a SeaTunnel user and want to see a demo of a specific synchronization scenario, please scroll to the bottom and leave a comment. We will prioritize recording demos of the most requested synchronization scenarios!

Demo Plan Goals

We aim to create a platform for sharing and learning. Through specific demo presentations and documentation, we hope to help community members better understand and apply various data connectors. These demos help beginners learn quickly and give seasoned experts a stage to showcase innovative solutions.

Previous Demo Tutorials:

Session 1: 【Demo 1】Syncing Data From MySQL to Doris Using SeaTunnel

Session 2: [Demo 2] Sync Data From MySQL CDC to Doris by Apache SeaTunnel

Description

Write data to Hive.

  • Tips

To use this connector, ensure your spark/flink cluster is already integrated with Hive. The tested hive version is 2.3.9.

If you use SeaTunnel Engine, You need to put seatunnel-hadoop3-3.1.4-uber.jar and hive-exec-3.1.3.jar and libfb303-0.9.3.jar in $SEATUNNEL_HOME/lib/ dir.

Key features

  • exactly-once

By default, we use 2PC commit to ensure exactly-once

  • file format
  • text
  • csv
  • parquet
  • orc
  • json
  • compress codec
  • lzo

Options

table_name [string]

Target Hive table name eg: db1.table1, and if the source is multiple modes, you can use ${database_name}.${table_name} to generate the table name, it will replace the ${database_name} and ${table_name} with the value of the CatalogTable generated from the source.

metastore_uri [string]

Hive metastore uri

hdfs_site_path [string]

The path of hdfs-site.xml, used to load the configuration of namenodes

hive_site_path [string]

The path of hive-site.xml

hive.hadoop.conf [map]

Properties in hadoop conf(‘core-site.xml’, ‘hdfs-site.xml’, ‘hive-site.xml’)

hive.hadoop.conf-path [string]

The specified loading path for the ‘core-site.xml’, ‘hdfs-site.xml’, ‘hive-site.xml’ files

krb5_path [string]

The path of krb5.conf, used to authenticate Kerberos

The path of hive-site.xml, used to authenticate hive metastore

kerberos_principal [string]

The principal of Kerberos

kerberos_keytab_path [string]

The keytab path of kerberos

abort_drop_partition_metadata [list]

Flag to decide whether to drop partition metadata from Hive Metastore during an abort operation. Note: this only affects the metadata in the metastore, the data in the partition will always be deleted(data generated during the synchronization process).

common options

Sink plugin common parameters, please refer to Sink Common Options for details

Example

Hive {
    table_name = "default.seatunnel_orc"
    metastore_uri = "thrift://namenode001:9083"
  }

Example 1

We have a source table like this:

create table test_hive_source(
     test_tinyint                          TINYINT,
     test_smallint                       SMALLINT,
     test_int                                INT,
     test_bigint                           BIGINT,
     test_boolean                       BOOLEAN,
     test_float                             FLOAT,
     test_double                         DOUBLE,
     test_string                           STRING,
     test_binary                          BINARY,
     test_timestamp                  TIMESTAMP,
     test_decimal                       DECIMAL(8,2),
     test_char                             CHAR(64),
     test_varchar                        VARCHAR(64),
     test_date                             DATE,
     test_array                            ARRAY<INT>,
     test_map                              MAP<STRING, FLOAT>,
     test_struct                           STRUCT<street:STRING, city:STRING, state:STRING, zip:INT>
     )
PARTITIONED BY (test_par1 STRING, test_par2 STRING);

We need to read data from the source table and write to another table:

create table test_hive_sink_text_simple(
     test_tinyint                          TINYINT,
     test_smallint                       SMALLINT,
     test_int                                INT,
     test_bigint                           BIGINT,
     test_boolean                       BOOLEAN,
     test_float                             FLOAT,
     test_double                         DOUBLE,
     test_string                           STRING,
     test_binary                          BINARY,
     test_timestamp                  TIMESTAMP,
     test_decimal                       DECIMAL(8,2),
     test_char                             CHAR(64),
     test_varchar                        VARCHAR(64),
     test_date                             DATE
     )
PARTITIONED BY (test_par1 STRING, test_par2 STRING);

The job config file can be like this:

env {
  parallelism = 3
  job.name="test_hive_source_to_hive"
}
source {
  Hive {
    table_name = "test_hive.test_hive_source"
    metastore_uri = "thrift://ctyun7:9083"
  }
}sink {
  # choose stdout output plugin to output data to console  Hive {
    table_name = "test_hive.test_hive_sink_text_simple"
    metastore_uri = "thrift://ctyun7:9083"
    hive.hadoop.conf = {
      bucket = "s3a://mybucket"
    }
}

Hive on s3

Step 1

Create the lib dir for the hive of emr.

mkdir -p ${SEATUNNEL_HOME}/plugins/Hive/lib

Step 2

Get the jars from the maven center to the lib.

cd ${SEATUNNEL_HOME}/plugins/Hive/lib
wget https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/2.6.5/hadoop-aws-2.6.5.jar
wget https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.9/hive-exec-2.3.9.jar

Step 3

Copy the jars from your environment on emr to the lib dir.

cp /usr/share/aws/emr/emrfs/lib/emrfs-hadoop-assembly-2.60.0.jar ${SEATUNNEL_HOME}/plugins/Hive/lib
cp /usr/share/aws/emr/hadoop-state-pusher/lib/hadoop-common-3.3.6-amzn-1.jar ${SEATUNNEL_HOME}/plugins/Hive/lib
cp /usr/share/aws/emr/hadoop-state-pusher/lib/javax.inject-1.jar ${SEATUNNEL_HOME}/plugins/Hive/lib
cp /usr/share/aws/emr/hadoop-state-pusher/lib/aopalliance-1.0.jar ${SEATUNNEL_HOME}/plugins/Hive/lib

Step 4

Run the case.

env {
  parallelism = 1
  job.mode = "BATCH"
}
source {
  FakeSource {
    schema = {
      fields {
        pk_id = bigint
        name = string
        score = int
      }
      primaryKey {
        name = "pk_id"
        columnNames = [pk_id]
      }
    }
    rows = [
      {
        kind = INSERT
        fields = [1, "A", 100]
      },
      {
        kind = INSERT
        fields = [2, "B", 100]
      },
      {
        kind = INSERT
        fields = [3, "C", 100]
      }
    ]
  }
}sink {
  Hive {
    table_name = "test_hive.test_hive_sink_on_s3"
    metastore_uri = "thrift://ip-192-168-0-202.cn-north-1.compute.internal:9083"
    hive.hadoop.conf-path = "/home/ec2-user/hadoop-conf"
    hive.hadoop.conf = {
       bucket="s3://ws-package"
    }
  }
}

Hive on oss

Step 1

Create the lib dir for hive of emr.

mkdir -p ${SEATUNNEL_HOME}/plugins/Hive/lib

Step 2

Get the jars from the maven center to the lib.

cd ${SEATUNNEL_HOME}/plugins/Hive/lib
wget https://repo1.maven.org/maven2/org/apache/hive/hive-exec/2.3.9/hive-exec-2.3.9.jar

Step 3

Copy the jars from your environment on emr to the lib dir and delete the conflicting jar.

cp -r /opt/apps/JINDOSDK/jindosdk-current/lib/jindo-*.jar ${SEATUNNEL_HOME}/plugins/Hive/lib
rm -f ${SEATUNNEL_HOME}/lib/hadoop-aliyun-*.jar

Step 4

Run the case.

env {
  parallelism = 1
  job.mode = "BATCH"
}
source {
  FakeSource {
    schema = {
      fields {
        pk_id = bigint
        name = string
        score = int
      }
      primaryKey {
        name = "pk_id"
        columnNames = [pk_id]
      }
    }
    rows = [
      {
        kind = INSERT
        fields = [1, "A", 100]
      },
      {
        kind = INSERT
        fields = [2, "B", 100]
      },
      {
        kind = INSERT
        fields = [3, "C", 100]
      }
    ]
  }
}sink {
  Hive {
    table_name = "test_hive.test_hive_sink_on_oss"
    metastore_uri = "thrift://master-1-1.c-1009b01725b501f2.cn-wulanchabu.emr.aliyuncs.com:9083"
    hive.hadoop.conf-path = "/tmp/hadoop"
    hive.hadoop.conf = {
        bucket="oss://emr-osshdfs.cn-wulanchabu.oss-dls.aliyuncs.com"
    }
  }
}

Example 2

We have multiple source tables like this:

create table test_1(
)
PARTITIONED BY (xx);
create table test_2(
)
PARTITIONED BY (xx);
...

We need to read data from these source tables and write to other tables:

The job config file can be like this:

env {
  # You can set flink configuration here
  parallelism = 3
  job.name="test_hive_source_to_hive"
}
source {
  Hive {
    tables_configs = [
      {
        table_name = "test_hive.test_1"
        metastore_uri = "thrift://ctyun6:9083"
      },
      {
        table_name = "test_hive.test_2"
        metastore_uri = "thrift://ctyun7:9083"
      }
    ]
  }
}sink {
  # choose stdout output plugin to output data to console
  Hive {
    table_name = "${database_name}.${table_name}"
    metastore_uri = "thrift://ctyun7:9083"
  }
}

About Apache SeaTunnel

Apache SeaTunnel is an easy-to-use, ultra-high-performance distributed data integration platform that supports real-time synchronization of massive amounts of data and can synchronize hundreds of billions of data per day stably and efficiently.

.

Welcome to fill out this form to be a speaker of Apache SeaTunnel: https://forms.gle/vtpQS6ZuxqXMt6DT6 :)

Why do we need Apache SeaTunnel?

Apache SeaTunnel does everything it can to solve the problems you may encounter in synchronizing massive amounts of data.

  • Data loss and duplication
  • Task buildup and latency
  • Low throughput
  • Long application-to-production cycle time
  • Lack of application status monitoring

Apache SeaTunnel Usage Scenarios

  • Massive data synchronization
  • Massive data integration
  • ETL of large volumes of data
  • Massive data aggregation
  • Multi-source data processing

Features of Apache SeaTunnel

  • Rich components
  • High scalability
  • Easy to use
  • Mature and stable

How to get started with Apache SeaTunnel quickly?

Want to experience Apache SeaTunnel quickly? SeaTunnel 2.1.0 takes 10 seconds to get you up and running.

https://seatunnel.apache.org/docs/2.1.0/developement/setup

How can I contribute?

We invite all partners who are interested in making local open-source global to join the Apache SeaTunnel contributors family and foster open-source together!

Submit an issue:

https://github.com/apache/seatunnel/issues

Contribute code to:

https://github.com/apache/seatunnel/pulls

Subscribe to the community development mailing list :

dev-subscribe@seatunnel.apache.org

Development Mailing List :

dev@seatunnel.apache.org

Join Slack:

https://join.slack.com/t/apacheseatunnel/shared_invite/zt-1kcxzyrxz-lKcF3BAyzHEmpcc4OSaCjQ

Follow Twitter:

https://twitter.com/ASFSeaTunnel

Join us now!❤️❤️

Demo
MySQL
Hive
Data Integration
Seatunnel

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Apache SeaTunnel

Written by Apache SeaTunnel

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