Version: 3.2

Change data through loading

Primary Key tables provided by StarRocks allow you to make data changes to StarRocks tables by running Stream Load, Broker Load, or Routine Load jobs. These data changes include inserts, updates, and deletions. However, Primary Key tables do not support changing data by using Spark Load or INSERT.

StarRocks also supports partial updates and conditional updates.

You can load data into StarRocks tables only as a user who has the INSERT privilege on those StarRocks tables. If you do not have the INSERT privilege, follow the instructions provided in GRANT to grant the INSERT privilege to the user that you use to connect to your StarRocks cluster. The syntax is GRANT INSERT ON TABLE <table_name> IN DATABASE <database_name> TO { ROLE <role_name> | USER <user_identity>}.

This topic uses CSV data as an example to describe how to make data changes to a StarRocks table through loading. The data file formats that are supported vary depending on the loading method of your choice.

NOTE

For CSV data, you can use a UTF-8 string, such as a comma (,), tab, or pipe (|), whose length does not exceed 50 bytes as a text delimiter.

Implementation

Primary Key tables provided by StarRocks support UPSERT and DELETE operations and does not distinguish INSERT operations from UPDATE operations.

When you create a load job, StarRocks supports adding a field named __op to the job creation statement or command. The __op field is used to specify the type of operation you want to perform.

NOTE

When you create a table, you do not need to add a column named __op to that table.

The method of defining the __op field varies depending on the loading method of your choice:

If you choose Stream Load, define the __op field by using the columns parameter.
If you choose Broker Load, define the __op field by using the SET clause.
If you choose Routine Load, define the __op field by using the COLUMNS column.

You can decide whether to add the __op field based on the data changes you want to make. If you do not add the __op field, the operation type defaults to UPSERT. The major data change scenarios are as follows:

If the data file you want to load involves only UPSERT operations, you do not need to add the __op field.
If the data file you want to load involves only DELETE operations, you must add the __op field and specify the operation type as DELETE.
If the data file you want to load involves both UPSERT and DELETE operations, you must add the __op field and make sure that the data file contains a column whose values are 0 or 1. A value of 0 indicates an UPSERT operation, and a value of 1 indicates a DELETE operation.

Usage notes

Make sure that each row in your data file has the same number of columns.
The columns that involve data changes must include the primary key column.

Prerequisites

Broker Load

See the "Background information" section in Load data from HDFS or Load data from cloud storage.

Routine load

If you choose Routine Load, make sure that topics are created in your Apache Kafka® cluster. Assume that you have created four topics: topic1, topic2, topic3, and topic4.

Basic operations

This section provides examples of how to make data changes to a StarRocks table through loading. For detailed syntax and parameter descriptions, see STREAM LOAD, BROKER LOAD, and CREATE ROUTINE LOAD.

UPSERT

If the data file you want to load involves only UPSERT operations, you do not need to add the __op field.

NOTE

If you add the __op field:

You can specify the operation type as UPSERT.

You can leave the __op field empty, because the operation type defaults to UPSERT.

Data examples

Prepare a data file.

a. Create a CSV file named example1.csv in your local file system. The file consists of three columns, which represent user ID, user name, and user score in sequence.
```
101,Lily,100
102,Rose,100
```
b. Publish the data of example1.csv to topic1 of your Kafka cluster.
Prepare a StarRocks table.

a. Create a Primary Key table named table1 in your StarRocks database test_db. The table consists of three columns: id, name, and score, of which id is the primary key.
```
CREATE TABLE `table1`
(
    `id` int(11) NOT NULL COMMENT "user ID",
    `name` varchar(65533) NOT NULL COMMENT "user name",
    `score` int(11) NOT NULL COMMENT "user score"
)
ENGINE=OLAP
PRIMARY KEY(`id`)
DISTRIBUTED BY HASH(`id`);
```
NOTE

Since v2.5.7, StarRocks can automatically set the number of buckets (BUCKETS) when you create a table or add a partition. You no longer need to manually set the number of buckets. For detailed information, see set the number of buckets.

b. Insert a record into table1.
```
INSERT INTO table1 VALUES
    (101, 'Lily',80);
```

Load data

Run a load job to update the record whose id is 101 in example1.csv to table1 and insert the record whose id is 102 in example1.csv into table1.

Run a Stream Load job.

If you do not want to include the __op field, run the following command:

curl --location-trusted -u <username>:<password> \
    -H "Expect:100-continue" \
    -H "label:label1" \
    -H "column_separator:," \
    -T example1.csv -XPUT \
    http://<fe_host>:<fe_http_port>/api/test_db/table1/_stream_load

If you want to include the __op field, run the following command:

curl --location-trusted -u <username>:<password> \
    -H "Expect:100-continue" \
    -H "label:label2" \
    -H "column_separator:," \
    -H "columns:__op ='upsert'" \
    -T example1.csv -XPUT \
    http://<fe_host>:<fe_http_port>/api/test_db/table1/_stream_load

Run a Broker Load job.

If you do not want to include the __op field, run the following command:

LOAD LABEL test_db.label1
(
    data infile("hdfs://<hdfs_host>:<hdfs_port>/example1.csv")
    into table table1
    columns terminated by ","
    format as "csv"
)
WITH BROKER;

If you want to include the __op field, run the following command:

LOAD LABEL test_db.label2
(
    data infile("hdfs://<hdfs_host>:<hdfs_port>/example1.csv")
    into table table1
    columns terminated by ","
    format as "csv"
    set (__op = 'upsert')
)
WITH BROKER;

Run a Routine Load job.

If you do not want to include the __op field, run the following command:

CREATE ROUTINE LOAD test_db.table1 ON table1
COLUMNS TERMINATED BY ",",
COLUMNS (id, name, score)
PROPERTIES
(
    "desired_concurrent_number" = "3",
    "max_batch_interval" = "20",
    "max_batch_rows"= "250000",
    "max_error_number" = "1000"
)
FROM KAFKA
(
    "kafka_broker_list" ="<kafka_broker_host>:<kafka_broker_port>",
    "kafka_topic" = "test1",
    "property.kafka_default_offsets" ="OFFSET_BEGINNING"
);

If you want to include the __op field, run the following command:

CREATE ROUTINE LOAD test_db.table1 ON table1
COLUMNS TERMINATED BY ",",
COLUMNS (id, name, score, __op ='upsert')
PROPERTIES
(
    "desired_concurrent_number" = "3",
    "max_batch_interval" = "20",
    "max_batch_rows"= "250000",
    "max_error_number" = "1000"
)
FROM KAFKA
(
    "kafka_broker_list" ="<kafka_broker_host>:<kafka_broker_port>",
    "kafka_topic" = "test1",
    "property.kafka_default_offsets" ="OFFSET_BEGINNING"
);

Query data

After the load is complete, query the data of table1 to verify that the load is successful:

SELECT * FROM table1;
+------+------+-------+
| id   | name | score |
+------+------+-------+
|  101 | Lily |   100 |
|  102 | Rose |   100 |
+------+------+-------+
2 rows in set (0.02 sec)

As shown in the preceding query result, the record whose id is 101 in example1.csv has been updated to table1, and the record whose id is 102 in example1.csv has been inserted into table1.

DELETE

If the data file you want to load involves only DELETE operations, you must add the __op field and specify the operation type as DELETE.

Data examples

Prepare a data file.

a. Create a CSV file named example2.csv in your local file system. The file consists of three columns, which represent user ID, user name, and user score in sequence.
```
101,Jack,100
```
b. Publish the data of example2.csv to topic2 of your Kafka cluster.
Prepare a StarRocks table.

a. Create a Primary Key table named table2 in your StarRocks table test_db. The table consists of three columns: id, name, and score, of which id is the primary key.
```
CREATE TABLE `table2`
      (
    `id` int(11) NOT NULL COMMENT "user ID",
    `name` varchar(65533) NOT NULL COMMENT "user name",
    `score` int(11) NOT NULL COMMENT "user score"
)
ENGINE=OLAP
PRIMARY KEY(`id`)
DISTRIBUTED BY HASH(`id`);
```
NOTE

Since v2.5.7, StarRocks can automatically set the number of buckets (BUCKETS) when you create a table or add a partition. You no longer need to manually set the number of buckets. For detailed information, see set the number of buckets.

b. Insert two records into table2.
```
INSERT INTO table2 VALUES
(101, 'Jack', 100),
(102, 'Bob', 90);
```

Load data

Run a load job to delete the record whose id is 101 in example2.csv from table2.

Run a Stream Load job.

curl --location-trusted -u <username>:<password> \
    -H "Expect:100-continue" \
    -H "label:label3" \
    -H "column_separator:," \
    -H "columns:__op='delete'" \
    -T example2.csv -XPUT \
    http://<fe_host>:<fe_http_port>/api/test_db/table2/_stream_load

Run a Broker Load job.

LOAD LABEL test_db.label3
(
    data infile("hdfs://<hdfs_host>:<hdfs_port>/example2.csv")
    into table table2
    columns terminated by ","
    format as "csv"
    set (__op = 'delete')
)
WITH BROKER;  

Run a Routine Load job.

CREATE ROUTINE LOAD test_db.table2 ON table2
COLUMNS(id, name, score, __op = 'delete')
PROPERTIES
(
    "desired_concurrent_number" = "3",
    "max_batch_interval" = "20",
    "max_batch_rows"= "250000",
    "max_error_number" = "1000"
)
FROM KAFKA
(
    "kafka_broker_list" ="<kafka_broker_host>:<kafka_broker_port>",
    "kafka_topic" = "test2",
    "property.kafka_default_offsets" ="OFFSET_BEGINNING"
);

Query data

After the load is complete, query the data of table2 to verify that the load is successful:

SELECT * FROM table2;
+------+------+-------+
| id   | name | score |
+------+------+-------+
|  102 | Bob  |    90 |
+------+------+-------+
1 row in set (0.00 sec)

As shown in the preceding query result, the record whose id is 101 in example2.csv has been deleted from table2.

UPSERT and DELETE

If the data file you want to load involves both UPSERT and DELETE operations, you must add the __op field and make sure that the data file contains a column whose values are 0 or 1. A value of 0 indicates an UPSERT operation, and a value of 1 indicates a DELETE operation.

Data examples

Prepare a data file.

a. Create a CSV file named example3.csv in your local file system. The file consists of four columns, which represent user ID, user name, user score, and operation type in sequence.
```
101,Tom,100,1
102,Sam,70,0
103,Stan,80,0
```
b. Publish the data of example3.csv to topic3 of your Kafka cluster.
Prepare a StarRocks table.

a. Create a Primary Key table named table3 in your StarRocks database test_db. The table consists of three columns: id, name, and score, of which id is the primary key.
```
CREATE TABLE `table3`
(
    `id` int(11) NOT NULL COMMENT "user ID",
    `name` varchar(65533) NOT NULL COMMENT "user name",
    `score` int(11) NOT NULL COMMENT "user score"
)
ENGINE=OLAP
PRIMARY KEY(`id`)
DISTRIBUTED BY HASH(`id`);
```
NOTE

Since v2.5.7, StarRocks can automatically set the number of buckets (BUCKETS) when you create a table or add a partition. You no longer need to manually set the number of buckets. For detailed information, see set the number of buckets.

b. Insert two records into table3.
```
INSERT INTO table3 VALUES
    (101, 'Tom', 100),
    (102, 'Sam', 90);
```

Load data

Run a load job to delete the record whose id is 101 in example3.csv from table3, update the record whose id is 102 in example3.csv to table3, and insert the record whose id is 103 in example3.csv into table3.

Run a Stream Load job:
```
curl --location-trusted -u <username>:<password> \
    -H "Expect:100-continue" \
    -H "label:label4" \
    -H "column_separator:," \
    -H "columns: id, name, score, temp, __op = temp" \
    -T example3.csv -XPUT \
    http://<fe_host>:<fe_http_port>/api/test_db/table3/_stream_load
```
NOTE

In the preceding example, the fourth column that represents the operation type in example3.csv is temporarily named as temp and the __op field is mapped onto the temp column by using the columns parameter. As such, StarRocks can decide whether to perform an UPSERT or DELETE operation depending on the value in the fourth column of example3.csv is 0 or 1.

Run a Broker Load job:

LOAD LABEL test_db.label4
(
    data infile("hdfs://<hdfs_host>:<hdfs_port>/example1.csv")
    into table table1
    columns terminated by ","
    format as "csv"
    (id, name, score, temp)
    set (__op=temp)
)
WITH BROKER;

Run a Routine Load job:

CREATE ROUTINE LOAD test_db.table3 ON table3
COLUMNS(id, name, score, temp, __op = temp)
PROPERTIES
(
    "desired_concurrent_number" = "3",
    "max_batch_interval" = "20",
    "max_batch_rows"= "250000",
    "max_error_number" = "1000"
)
FROM KAFKA
(
    "kafka_broker_list" = "<kafka_broker_host>:<kafka_broker_port>",
    "kafka_topic" = "test3",
    "property.kafka_default_offsets" = "OFFSET_BEGINNING"
);

Query data

After the load is complete, query the data of table3 to verify that the load is successful:

SELECT * FROM table3;
+------+------+-------+
| id   | name | score |
+------+------+-------+
|  102 | Sam  |    70 |
|  103 | Stan |    80 |
+------+------+-------+
2 rows in set (0.01 sec)

As shown in the preceding query result, the record whose id is 101 in example3.csv has been deleted from table3, the record whose id is 102 in example3.csv has been updated to table3, and the record whose id is 103 in example3.csv has been inserted into table3.

Partial updates

Primary Key tables also support partial updates, and provide two modes of partial updates, row mode and column mode, for different data update scenarios. These two modes of partial updates can minimize the overhead of partial updates as much as possible while guaranteeing query performance, ensuring real-time updates. Row mode is more suitable for real-time update scenarios involving many columns and small batches. Column mode is suitable for batch processing update scenarios involving a few columns and a large number of rows.

NOTICE

When you perform a partial update, if the row to be updated does not exist, StarRocks inserts a new row, and fills default values in fields that are empty because no data updates are inserted into them.

This section uses CSV as an example to describe how to perform partial updates.

Data examples

Prepare a data file.

a. Create a CSV file named example4.csv in your local file system. The file consists of two columns, which represent user ID and user name in sequence.
```
101,Lily
102,Rose
103,Alice
```
b. Publish the data of example4.csv to topic4 of your Kafka cluster.
Prepare a StarRocks table.

a. Create a Primary Key table named table4 in your StarRocks database test_db. The table consists of three columns: id, name, and score, of which id is the primary key.
```
CREATE TABLE `table4`
(
    `id` int(11) NOT NULL COMMENT "user ID",
    `name` varchar(65533) NOT NULL COMMENT "user name",
    `score` int(11) NOT NULL COMMENT "user score"
)
ENGINE=OLAP
PRIMARY KEY(`id`)
DISTRIBUTED BY HASH(`id`);
```
NOTE

Since v2.5.7, StarRocks can automatically set the number of buckets (BUCKETS) when you create a table or add a partition. You no longer need to manually set the number of buckets. For detailed information, see set the number of buckets.

b. Insert a record into table4.
```
INSERT INTO table4 VALUES
    (101, 'Tom',80);
```

Load data

Run a load to update the data in the two columns of example4.csv to the id and name columns of table4.

Run a Stream Load job:
```
curl --location-trusted -u <username>:<password> \
    -H "Expect:100-continue" \
    -H "label:label7" -H "column_separator:," \
    -H "partial_update:true" \
    -H "columns:id,name" \
    -T example4.csv -XPUT \
    http://<fe_host>:<fe_http_port>/api/test_db/table4/_stream_load
```
NOTE

If you choose Stream Load, you must set the partial_update parameter to true to enable the partial update feature. The default is partial updates in row mode. If you need to perform partial updates in column mode, you need to set partial_update_mode to column. Additionally, you must use the columns parameter to specify the columns you want to update.
Run a Broker Load job:
```
LOAD LABEL test_db.table4
(
    data infile("hdfs://<hdfs_host>:<hdfs_port>/example4.csv")
    into table table4
    format as "csv"
    (id, name)
)
WITH BROKER
PROPERTIES
(
    "partial_update" = "true"
);
```
NOTE

If you choose Broker Load, you must set the partial_update parameter to true to enable the partial update feature. The default is partial updates in row mode. If you need to perform partial updates in column mode, you need to set partial_update_mode to column. Additionally, you must use the column_list parameter to specify the columns you want to update.
Run a Routine Load job:
```
CREATE ROUTINE LOAD test_db.table4 on table4
COLUMNS (id, name),
COLUMNS TERMINATED BY ','
PROPERTIES
(
    "partial_update" = "true"
)
FROM KAFKA
(
    "kafka_broker_list" ="<kafka_broker_host>:<kafka_broker_port>",
    "kafka_topic" = "test4",
    "property.kafka_default_offsets" ="OFFSET_BEGINNING"
);
```
NOTE
- If you choose Routine Load, you must set the partial_update parameter to true to enable the partial update feature. Additionally, you must use the COLUMNS parameter to specify the columns you want to update.
- Routine Load only supports partial updates in row modes and does not support partial updates in column mode.

Query data

After the load is complete, query the data of table4 to verify that the load is successful:

SELECT * FROM table4;
+------+-------+-------+
| id   | name  | score |
+------+-------+-------+
|  102 | Rose  |     0 |
|  101 | Lily  |    80 |
|  103 | Alice |     0 |
+------+-------+-------+
3 rows in set (0.01 sec)

As shown in the preceding query result, the record whose id is 101 in example4.csv has been updated to table4, and the records whose id are 102 and 103 in example4.csv have been Inserted into table4.

Conditional updates

From StarRocks v2.5 onwards, Primary Key tables support conditional updates. You can specify a non-primary key column as the condition to determine whether updates can take effect. As such, the update from a source record to a destination record takes effect only when the source data record has a greater or equal value than the destination data record in the specified column.

The conditional update feature is designed to resolve data disorder. If the source data is disordered, you can use this feature to ensure that new data will not be overwritten by old data.

NOTICE

You cannot specify different columns as update conditions for the same batch of data.

DELETE operations do not support conditional updates.

In versions earlier than v3.1.3, partial updates and conditional updates cannot be used simultaneously. From v3.1.3 onwards, StarRocks supports using partial updates with conditional updates.

Data examples

Prepare a data file.

a. Create a CSV file named example5.csv in your local file system. The file consists of three columns, which represent user ID, version, and user score in sequence.
```
101,1,100
102,3,100
```
b. Publish the data of example5.csv to topic5 of your Kafka cluster.
Prepare a StarRocks table.

a. Create a Primary Key table named table5 in your StarRocks database test_db. The table consists of three columns: id, version, and score, of which id is the primary key.
```
CREATE TABLE `table5`
(
    `id` int(11) NOT NULL COMMENT "user ID", 
    `version` int NOT NULL COMMENT "version",
    `score` int(11) NOT NULL COMMENT "user score"
)
ENGINE=OLAP
PRIMARY KEY(`id`) DISTRIBUTED BY HASH(`id`);
```
NOTE

Since v2.5.7, StarRocks can automatically set the number of buckets (BUCKETS) when you create a table or add a partition. You no longer need to manually set the number of buckets. For detailed information, see set the number of buckets.

b. Insert a record into table5.
```
INSERT INTO table5 VALUES
    (101, 2, 80),
    (102, 2, 90);
```

Load data

Run a load to update the records whose id values are 101 and 102, respectively, from example5.csv into table5, and specify that the updates take effect only when the version value in each of the two records is greater or equal to their current version values.

Run a Stream Load job:

curl --location-trusted -u <username>:<password> \
    -H "Expect:100-continue" \
    -H "label:label10" \
    -H "column_separator:," \
    -H "merge_condition:version" \
    -T example5.csv -XPUT \
    http://<fe_host>:<fe_http_port>/api/test_db/table5/_stream_load

Run a Routine Load job:

CREATE ROUTINE LOAD test_db.table5 on table5
COLUMNS (id, version, score),
COLUMNS TERMINATED BY ','
PROPERTIES
(
    "merge_condition" = "version"
)
FROM KAFKA
(
    "kafka_broker_list" ="<kafka_broker_host>:<kafka_broker_port>",
    "kafka_topic" = "topic5",
    "property.kafka_default_offsets" ="OFFSET_BEGINNING"
);

Run a Broker Load job:

LOAD LABEL test_db.table5
( DATA INFILE ("s3://xxx.csv")
  INTO TABLE table5 COLUMNS TERMINATED BY "," FORMAT AS "CSV"
)
WITH BROKER
PROPERTIES
(
    "merge_condition" = "version"
);

Query data

After the load is complete, query the data of table5 to verify that the load is successful:

SELECT * FROM table5;
+------+------+-------+
| id   | version | score |
+------+------+-------+
|  101 |       2 |   80 |
|  102 |       3 |  100 |
+------+------+-------+
2 rows in set (0.02 sec)

As shown in the preceding query result, the record whose id is 101 in example5.csv is not updated to table5, and the record whose id is 102 in example5.csv has been Inserted into table5.

Change data through loading

Implementation​

Usage notes​

Prerequisites​

Broker Load​

Routine load​

Basic operations​

UPSERT​

Data examples​

Load data​

Query data​

DELETE​

Data examples​

Load data​

Query data​

UPSERT and DELETE​

Data examples​

Load data​

Query data​

Partial updates​

Data examples​

Load data​

Query data​

Conditional updates​

Data examples​

Load data​

Query data​

What did you think of this doc?

Implementation

Usage notes

Prerequisites

Broker Load

Routine load

Basic operations

UPSERT

Data examples

Load data

Query data

DELETE

Data examples

Load data

Query data

UPSERT and DELETE

Data examples

Load data

Query data

Partial updates

Data examples

Load data

Query data

Conditional updates

Data examples

Load data

Query data