Orc
Function
The Apache Orc format allows to read and write Orc data. For details, see Orc Format.
Supported Connectors
- FileSystem
Parameter Description
Parameter |
Mandatory |
Default Value |
Data Type |
Description |
---|---|---|---|---|
format |
Yes |
None |
String |
Specify what format to use, here should be orc. |
Orc format also supports table properties from Table properties. For example, you can configure orc.compress=SNAPPY to enable snappy compression.
Data Type Mapping
Orc format type mapping is compatible with Apache Hive. The following table lists the type mapping from Flink type to Orc type.
Flink SQL Type |
Orc Physical Type |
Orc Logical Type |
---|---|---|
CHAR |
bytes |
CHAR |
VARCHAR |
bytes |
VARCHAR |
STRING |
bytes |
STRING |
BOOLEAN |
long |
BOOLEAN |
BYTES |
bytes |
BINARY |
DECIMAL |
decimal |
DECIMAL |
TINYINT |
long |
BYTE |
SMALLINT |
long |
SHORT |
INT |
long |
INT |
BIGINT |
long |
LONG |
FLOAT |
double |
FLOAT |
DOUBLE |
double |
DOUBLE |
DATE |
long |
DATE |
TIMESTAMP |
timestamp |
TIMESTAMP |
ARRAY |
- |
LIST |
MAP |
- |
MAP |
ROW |
- |
STRUCT |
Example
Use Kafka to send data and output the data to Print.
- Create a datasource connection for the communication with the VPC and subnet where Kafka locates and bind the connection to the queue. Set a security group and inbound rule to allow access of the queue and test the connectivity of the queue using the Kafka IP address. For example, locate a general-purpose queue where the job runs and choose More > Test Address Connectivity in the Operation column. If the connection is successful, the datasource is bound to the queue. Otherwise, the binding fails.
- Create a Flink OpenSource SQL job and enable checkpointing. Copy the following statement and submit the job:
CREATE TABLE kafkaSource ( order_id string, order_channel string, order_time string, pay_amount double, real_pay double, pay_time string, user_id string, user_name string, area_id string ) WITH ( 'connector' = 'kafka', 'topic-pattern' = kafkaTopic', 'properties.bootstrap.servers' = 'KafkaAddress1:KafkaPort,KafkaAddress2:KafkaPort', 'properties.group.id' = 'GroupId'', 'scan.startup.mode' = 'latest-offset', 'format' = 'csv' ); CREATE TABLE sink ( order_id string, order_channel string, order_time string, pay_amount double, real_pay double, pay_time string, user_id string, user_name string, area_id string ) WITH ( 'connector' = 'filesystem', 'format' = 'orc', 'path' = 'obs://xx' ); insert into sink select * from kafkaSource;
- Insert the following data into the source Kafka topic:
202103251505050001,appshop,2021-03-25 15:05:05,500.00,400.00,2021-03-25 15:10:00,0003,Cindy,330108 202103241606060001,appShop,2021-03-24 16:06:06,200.00,180.00,2021-03-24 16:10:06,0001,Alice,330106
- Read the ORC file in the OBS path configured in the sink table. The data results are as follows:
202103251202020001, miniAppShop, 2021-03-25 12:02:02, 60.0, 60.0, 2021-03-25 12:03:00, 0002, Bob, 330110 202103241606060001, appShop, 2021-03-24 16:06:06, 200.0, 180.0, 2021-03-24 16:10:06, 0001, Alice, 330106
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