- Releases Notes and Announcements
- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- SQL Developer Guide
- Python Developer Guide
- ETL Developer Guide
- FAQ
- Contact Us
- Releases Notes and Announcements
- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- SQL Developer Guide
- Python Developer Guide
- ETL Developer Guide
- FAQ
- Contact Us
Versions
Flink Version | Description |
1.11 | Unsupported |
1.13 | Supported (use as source and sink) |
1.14 | Unsupported |
1.16 | Unsupported |
Use cases
This connector can be used as a source or a sink.
Defining a table in DDL
As a sink:
CREATE TABLE hudi_sink(uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,name VARCHAR(10),age INT,ts TIMESTAMP(3),`partition` VARCHAR(20)) WITH ('connector' = 'hudi', 'path' = 'hdfs://HDFS1000/data/hudi/mor', 'table.type' = 'MERGE_ON_READ' -- The MERGE_ON_READ table, which defaults to `COPY_ON_WRITE`., 'write.tasks' = '3' -- Default value: 4., 'compaction.tasks' = '4' -- Default value: 4.-- , 'hive_sync.enable' = 'true' -- Default value: false.-- , 'hive_sync.db' = 'default'-- , 'hive_sync.table' = 'datagen_mor_1'-- , 'hive_sync.mode' = 'jdbc'-- , 'hive_sync.username' = ''-- , 'hive_sync.password' = ''-- , 'hive_sync.jdbc_url' = 'jdbc:hive2://172.28.1.185:7001'-- , 'hive_sync.metastore.uris' = 'thrift://172.28.1.185:7004');
As a source:
CREATE TABLE `source`(uuid VARCHAR(20) PRIMARY KEY NOT ENFORCED,name VARCHAR(10),age INT,ts TIMESTAMP(3),`partition` VARCHAR(20)) WITH ('connector' = 'hudi', 'path' = 'hdfs://172.28.28.202:4007/path/hudidata', 'table.type' = 'MERGE_ON_READ' -- The MOR table. Its incremental data cannot be read., 'read.tasks' = '1' -- The parallelism of the read tasks, which defaults to 4., 'hoodie.datasource.query.type' = 'snapshot' -- Default value: snapshot; other valid values: read_optimized and incremental., 'read.streaming.enabled' = 'true' -- This option enables the streaming read., 'read.start-commit' = 'earliest' -- Specifies the start commit instant time; the commit time format should be 'yyyyMMddHHmmss'., 'read.streaming.check-interval' = '4');
WITH parameters
Common parameters
Option | Required | Default Value | Description |
connector | Yes | None | Here, it should be hudi. |
path | Yes | None | The data storage path, in the format of hdfs:// for data storage in HDFS and COSN://$bucket/$path for data storage in COS. |
Parameters when as a sink
Option | Required | Default Value | Description |
table.type | No | COPY_ON_WRITE | The Hudi table type. Valid values: COPY_ON_WRITE and MERGE_ON_READ. |
HoodieRecord parameters
Option | Required | Default Value | Description |
hoodie.datasource.write.recordkey.field | No | uuid | The key field. If the Flink table has a primary key, use it. |
hoodie.datasource.write.partitionpath.field | No | "" | The partition path field. Null indicates the table is not partitioned. |
write.precombine.field | No | ts | Field used in pre-combining before actual write. When two records have the same key value, the one with the larger value will be picked for the pre-combine field, determined by Object.compareTo(..). |
Parallelism parameters
Option | Required | Default Value | Description |
write.tasks | No | 4 | The parallelism of tasks that do actual write. |
write.index_bootstrap.tasks | No | None | The parallelism of tasks that do index bootstrap, which defaults to the parallelism of the execution environment. |
write.bucket_assign.tasks | No | None | The parallelism of tasks that do bucket assign, which defaults to the parallelism of the execution environment. |
compaction.tasks | No | 4 | The parallelism of tasks that do actual compaction. |
Compaction parameters
Option | Required | Default Value | Description |
compaction.schedule.enabled | No | true | Whether to enable compaction. |
compaction.async.enabled | No | true | Whether to use async compaction. |
compaction.trigger.strategy | No | num_commits | num_commits / time_elapsed / num_and_time / num_or_time |
Hive metadata sync parameters
Option | Required | Default Value | Description |
hive_sync.enable | No | false | - |
hive_sync.db | No | - | - |
hive_sync.table | No | - | - |
hive_sync.mode | No | jdbc | Valid values: hms, jdbc, and hiveql. |
hive_sync.username | No | - | - |
hive_sync.password | No | - | - |
hive_sync.jdbc_url | No | - | - |
More parameters
Parameters when as a source
Option | Required | Default Value | Description |
read.tasks | No | 4 | The parallelism of tasks that do actual read. |
hoodie.datasource.query.type | No | snapshot | Valid values: snapshot, read_optimized, and incremental. |
read.streaming.enabled | No | false | - |
read.streaming.check-interval | No | 60 | The check interval for streaming read in seconds. |
read.streaming.skip_compaction | No | false | - |
read.start-commit | No | None | The start commit instant for reading in the format of 'yyyyMMddHHmmss'. It can be set to earliest for reading from the earliest instant for streaming read. |
read.end-commit | No | None | The end commit instant for reading, which does not need to be specifically set. |
More parameters
COS configurations
No additional configurations are required. You just need to set
path to the respective cosn path.HDFS configurations
Getting the HDFS JAR package
To write data to Hudi in a Flink SQL task, if the data is stored in HDFS, a JAR package containing HDFS configurations is required to connect Flink to the target HDFS cluster. The steps to get the JAR package and to use it are as follows:
1. Log in to the respective Hive cluster using SSH.
2. Get
hive-site.xml and hdfs-site.xml from the following paths in the EMR Hive cluster./usr/local/service/hadoop/etc/hadoop/hdfs-site.xml
3. Package the obtained configuration files in a JAR package.
jar -cvf hdfs-xxx.jar hdfs-site.xml
4. Check the JAR structure (you can run a Vim command to view it). Make sure the JAR file includes the following information and has the correct structure.
vi hdfs-xxx.jar
META-INF/META-INF/MANIFEST.MFhdfs-site.xml
Setting the HDFS user
Note
By default, Flink jobs access HDFS with a Flink user. If the Flink user does not have permission to write to HDFS, you can use advanced job parameters to set the accessing user to a user that has write permission or to the super-user
hadoop.containerized.taskmanager.env.HADOOP_USER_NAME: hadoopcontainerized.master.env.HADOOP_USER_NAME: hadoop
Kerberos authentication
1. Log in to the cluster master node to get the files
krb5.conf, emr.keytab, core-site.xml, and hdfs-site.xml in the following paths./etc/krb5.conf/var/krb5kdc/emr.keytab/usr/local/service/hadoop/etc/hadoop/core-site.xml/usr/local/service/hadoop/etc/hadoop/hdfs-site.xml
2. Package the obtained configuration files in a JAR package.
jar cvf hdfs-xxx.jar krb5.conf emr.keytab core-site.xml hdfs-site.xml
3. Check the JAR structure (run the Vim command
vim hdfs-xxx.jar). Make sure the JAR file includes the following information and has the correct structure.META-INF/META-INF/MANIFEST.MFemr.keytabkrb5.confhdfs-site.xmlcore-site.xml
4. Upload the JAR file to the Dependencies page of the Stream Compute Service console, and reference the package when configuring job parameters.
5. Get the Kerberos principal and configure it in advanced job parameters.
klist -kt /var/krb5kdc/emr.keytab# The output is as follows (use the first): hadoop/172.28.28.51@EMR-OQPO48B9KVNO Timestamp Principal---- ------------------- ------------------------------------------------------2 08/09/2021 15:34:40 hadoop/172.28.28.51@EMR-OQPO48B92 08/09/2021 15:34:40 HTTP/172.28.28.51@EMR-OQPO48B92 08/09/2021 15:34:40 hadoop/VM-28-51-centos@EMR-OQPO48B92 08/09/2021 15:34:40 HTTP/VM-28-51-centos@EMR-OQPO48B9
6. Configure the principal in advanced job parameters.
containerized.taskmanager.env.HADOOP_USER_NAME: hadoopcontainerized.master.env.HADOOP_USER_NAME: hadoopsecurity.kerberos.login.principal: hadoop/172.28.28.51@EMR-OQPO48B9security.kerberos.login.keytab: emr.keytabsecurity.kerberos.login.conf: krb5.conf
FAQs
Hive sync
The Hive table sync failed, with an error raised.
java.lang.ClassNotFoundException: org.apache.hudi.hadoop.HoodieParquetInputFormat
Check whether the Hive environment contains the JAR package required for Hudi. For details, see Hive.
Yes
No
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