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Version: 2.3.8

Hive

Hive sink connector

Description​

Write data to Hive.

tip

In order to use this connector, You must ensure your spark/flink cluster already integrated hive. The tested hive version is 2.3.9.

If you use SeaTunnel Engine, You need 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​

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

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

Options​

nametyperequireddefault value
table_namestringyes-
metastore_uristringyes-
compress_codecstringnonone
hdfs_site_pathstringno-
hive_site_pathstringno-
hive.hadoop.confMapno-
hive.hadoop.conf-pathstringno-
krb5_pathstringno/etc/krb5.conf
kerberos_principalstringno-
kerberos_keytab_pathstringno-
abort_drop_partition_metadatabooleannotrue
common-optionsno-

table_name [string]​

Target Hive table name eg: db1.table1, and if the source is multiple mode, 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 generate from the source.

metastore_uri [string]​

Hive metastore uri

hdfs_site_path [string]​

The path of hdfs-site.xml, used to load ha 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 authentication kerberos

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

kerberos_principal [string]​

The principal of kerberos

kerberos_keytab_path [string]​

The keytab path of kerberos

abort_drop_partition_metadata [boolean]​

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 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 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"
fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
}
}

Hive on s3​

Step 1​

Create the lib dir for hive of emr.

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

Step 2​

Get the jars from 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"
fs.s3a.aws.credentials.provider="com.amazonaws.auth.InstanceProfileCredentialsProvider"
}
}
}

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 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 table like this:

create table test_1(
)
PARTITIONED BY (xx);

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

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

The job config file can 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"
}
}

Changelog​

2.2.0-beta 2022-09-26​

  • Add Hive Sink Connector

2.3.0-beta 2022-10-20​

  • [Improve] Hive Sink supports automatic partition repair (3133)

2.3.0 2022-12-30​

  • [BugFix] Fixed the following bugs that failed to write data to files (3258)
    • When field from upstream is null it will throw NullPointerException
    • Sink columns mapping failed
    • When restore writer from states getting transaction directly failed

Next version​

  • [Improve] Support kerberos authentication (3840)
  • [Improve] Added partition_dir_expression validation logic (3886)