Version: Stable-3.1

Continuously load data from Apache Flink®

StarRocks provides a self-developed connector named StarRocks Connector for Apache Flink® (Flink connector for short) to help you load data into a StarRocks table by using Flink. The basic principle is to accumulate the data and then load it all at a time into StarRocks through STREAM LOAD.

The Flink connector supports DataStream API, Table API & SQL, and Python API. It has a higher and more stable performance than flink-connector-jdbc provided by Apache Flink®.

NOTICE

Loading data into StarRocks tables with Flink connector needs SELECT and INSERT privileges. If you do not have these privileges, follow the instructions provided in GRANT to grant these privileges to the user that you use to connect to your StarRocks cluster.

Version requirements

Connector	Flink	StarRocks	Java	Scala
1.2.10	1.15,1.16,1.17,1.18,1.19	2.1 and later	8	2.11,2.12
1.2.9	1.15,1.16,1.17,1.18	2.1 and later	8	2.11,2.12
1.2.8	1.13,1.14,1.15,1.16,1.17	2.1 and later	8	2.11,2.12
1.2.7	1.11,1.12,1.13,1.14,1.15	2.1 and later	8	2.11,2.12

Obtain Flink connector

You can obtain the Flink connector JAR file in the following ways:

Directly download the compiled Flink connector JAR file.
Add the Flink connector as a dependency in your Maven project and then download the JAR file.
Compile the source code of the Flink connector into a JAR file by yourself.

The naming format of the Flink connector JAR file is as follows:

Since Flink 1.15, it's flink-connector-starrocks-${connector_version}_flink-${flink_version}.jar. For example, if you install Flink 1.15 and you want to use Flink connector 1.2.7, you can use flink-connector-starrocks-1.2.7_flink-1.15.jar.
Prior to Flink 1.15, it's flink-connector-starrocks-${connector_version}_flink-${flink_version}_${scala_version}.jar. For example, if you install Flink 1.14 and Scala 2.12 in your environment, and you want to use Flink connector 1.2.7, you can use flink-connector-starrocks-1.2.7_flink-1.14_2.12.jar.

NOTICE

In general, the latest version of the Flink connector only maintains compatibility with the three most recent versions of Flink.

Download the compiled Jar file

Directly download the corresponding version of the Flink connector Jar file from the Maven Central Repository.

Maven Dependency

In your Maven project's pom.xml file, add the Flink connector as a dependency according to the following format. Replace flink_version, scala_version, and connector_version with the respective versions.

In Flink 1.15 and later

<dependency>
    <groupId>com.starrocks</groupId>
    <artifactId>flink-connector-starrocks</artifactId>
    <version>${connector_version}_flink-${flink_version}</version>
</dependency>

In versions earlier than Flink 1.15

<dependency>
    <groupId>com.starrocks</groupId>
    <artifactId>flink-connector-starrocks</artifactId>
    <version>${connector_version}_flink-${flink_version}_${scala_version}</version>
</dependency>

Compile by yourself

Download the Flink connector package.
Execute the following command to compile the source code of Flink connector into a JAR file. Note that flink_version is replaced with the corresponding Flink version.
```
sh build.sh <flink_version>
```
For example, if the Flink version in your environment is 1.15, you need to execute the following command:
```
sh build.sh 1.15
```
Go to the target/ directory to find the Flink connector JAR file, such as flink-connector-starrocks-1.2.7_flink-1.15-SNAPSHOT.jar, generated upon compilation.

NOTE

The name of Flink connector which is not formally released contains the SNAPSHOT suffix.

Options

Option	Required	Default value	Description
connector	Yes	NONE	The connector that you want to use. The value must be "starrocks".
jdbc-url	Yes	NONE	The address that is used to connect to the MySQL server of the FE. You can specify multiple addresss, which must be separated by a comma (,). Format: `jdbc:mysql://<fe_host1>:<fe_query_port1>,<fe_host2>:<fe_query_port2>,<fe_host3>:<fe_query_port3>`.
load-url	Yes	NONE	The HTTP URL of the FE in your StarRocks cluster. You can specify multiple URLs, which must be separated by a semicolon (;). Format: `<fe_host1>:<fe_http_port1>;<fe_host2>:<fe_http_port2>`.
database-name	Yes	NONE	The name of the StarRocks database into which you want to load data.
table-name	Yes	NONE	The name of the table that you want to use to load data into StarRocks.
username	Yes	NONE	The username of the account that you want to use to load data into StarRocks. The account needs SELECT and INSERT privileges.
password	Yes	NONE	The password of the preceding account.
sink.semantic	No	at-least-once	The semantic guaranteed by sink. Valid values: at-least-once and exactly-once.
sink.version	No	AUTO	The interface used to load data. This parameter is supported from Flink connector version 1.2.4 onwards. `V1`: Use Stream Load interface to load data. Connectors before 1.2.4 only support this mode. `V2`: Use Stream Load transaction interface to load data. It requires StarRocks to be at least version 2.4. Recommends `V2` because it optimizes the memory usage and provides a more stable exactly-once implementation. `AUTO`: If the version of StarRocks supports transaction Stream Load, will choose `V2` automatically, otherwise choose `V1`
sink.label-prefix	No	NONE	The label prefix used by Stream Load. Recommend to configure it if you are using exactly-once with connector 1.2.8 and later. See exactly-once usage notes.
sink.buffer-flush.max-bytes	No	94371840(90M)	The maximum size of data that can be accumulated in memory before being sent to StarRocks at a time. The maximum value ranges from 64 MB to 10 GB. Setting this parameter to a larger value can improve loading performance but may increase loading latency.
sink.buffer-flush.max-rows	No	500000	The maximum number of rows that can be accumulated in memory before being sent to StarRocks at a time. This parameter is available only when you set `sink.version` to `V1` and set `sink.semantic` to `at-least-once`. Valid values: 64000 to 5000000.
sink.buffer-flush.interval-ms	No	300000	The interval at which data is flushed. This parameter is available only when you set `sink.semantic` to `at-least-once`. Valid values: 1000 to 3600000. Unit: ms.
sink.max-retries	No	3	The number of times that the system retries to perform the Stream Load job. This parameter is available only when you set `sink.version` to `V1`. Valid values: 0 to 10.
sink.connect.timeout-ms	No	30000	The timeout for establishing HTTP connection. Valid values: 100 to 60000. Unit: ms. Before Flink connector v1.2.9, the default value is `1000`.
sink.socket.timeout-ms	No	-1	Supported since 1.2.10. The time duration for which the HTTP client waits for data. Unit: ms. The default value `-1` means there is no timeout.
sink.wait-for-continue.timeout-ms	No	10000	Supported since 1.2.7. The timeout for waiting response of HTTP 100-continue from the FE. Valid values: `3000` to `60000`. Unit: ms
sink.ignore.update-before	No	true	Supported since version 1.2.8. Whether to ignore `UPDATE_BEFORE` records from Flink when loading data to Primary Key tables. If this parameter is set to false, the record is treated as a delete operation to StarRocks table.
sink.properties.*	No	NONE	The parameters that are used to control Stream Load behavior. For example, the parameter `sink.properties.format` specifies the format used for Stream Load, such as CSV or JSON. For a list of supported parameters and their descriptions, see STREAM LOAD.
sink.properties.format	No	csv	The format used for Stream Load. The Flink connector will transform each batch of data to the format before sending them to StarRocks. Valid values: `csv` and `json`.
sink.properties.row_delimiter	No	\n	The row delimiter for CSV-formatted data.
sink.properties.column_separator	No	\t	The column separator for CSV-formatted data.
sink.properties.max_filter_ratio	No	0	The maximum error tolerance of the Stream Load. It's the maximum percentage of data records that can be filtered out due to inadequate data quality. Valid values: `0` to `1`. Default value: `0`. See Stream Load for details.
sink.parallelism	No	NONE	The parallelism of the connector. Only available for Flink SQL. If not set, Flink planner will decide the parallelism. In the scenario of multi-parallelism, users need to guarantee data is written in the correct order.
sink.properties.strict_mode	No	false	Specifies whether to enable the strict mode for Stream Load. It affects the loading behavior when there are unqualified rows, such as inconsistent column values. Valid values: `true` and `false`. Default value: `false`. See Stream Load for details.
sink.properties.compression	No	NONE	Supported since 1.2.10. The compression algorithm used for Stream Load. Currently, compression is only supported for the JSON format. Valid values: `lz4_frame`. Compression for the JSON format is supported only in StarRocks v3.2.7 and later.

Data type mapping between Flink and StarRocks

Flink data type	StarRocks data type
BOOLEAN	BOOLEAN
TINYINT	TINYINT
SMALLINT	SMALLINT
INTEGER	INTEGER
BIGINT	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
DECIMAL	DECIMAL
BINARY	INT
CHAR	STRING
VARCHAR	STRING
STRING	STRING
DATE	DATE
TIMESTAMP_WITHOUT_TIME_ZONE(N)	DATETIME
TIMESTAMP_WITH_LOCAL_TIME_ZONE(N)	DATETIME
ARRAY<T>	ARRAY<T>
MAP<KT,VT>	JSON STRING
ROW<arg T...>	JSON STRING

Usage notes

Exactly Once

If you want sink to guarantee exactly-once semantics, we recommend you to upgrade StarRocks to 2.5 or later, and Flink connector to 1.2.4 or later
- Since Flink connector 1.2.4, the exactly-once is redesigned based on Stream Load transaction interface provided by StarRocks since 2.4. Compared to the previous implementation based on non-transactional Stream Load non-transactional interface, the new implementation reduces memory usage and checkpoint overhead, thereby enhancing real-time performance and stability of loading.
- If the version of StarRocks is earlier than 2.4 or the version of Flink connector is earlier than 1.2.4, the sink will automatically choose the implementation based on Stream Load non-transactional interface.
Configurations to guarantee exactly-once
- The value of sink.semantic needs to be exactly-once.
- If the version of Flink connector is 1.2.8 and later, it is recommended to specify the value of sink.label-prefix. Note that the label prefix must be unique among all types of loading in StarRocks, such as Flink jobs, Routine Load, and Broker Load.
  - If the label prefix is specified, the Flink connector will use the label prefix to clean up lingering transactions that may be generated in some Flink failure scenarios, such as the Flink job fails when a checkpoint is still in progress. These lingering transactions are generally in PREPARED status if you use SHOW PROC '/transactions/<db_id>/running'; to view them in StarRocks. When the Flink job restores from checkpoint, the Flink connector will find these lingering transactions according to the label prefix and some information in checkpoint, and abort them. The Flink connector can not abort them when the Flink job exits because of the two-phase-commit mechanism to implement the exactly-once. When the Flink job exits, the Flink connector has not received the notification from Flink checkpoint coordinator whether the transactions should be included in a successful checkpoint, and it may lead to data loss if these transactions are aborted anyway. You can have an overview about how to achieve end-to-end exactly-once in Flink in this blogpost.
  - If the label prefix is not specified, lingering transactions will be cleaned up by StarRocks only after they time out. However the number of running transactions can reach the limitation of StarRocks max_running_txn_num_per_db if Flink jobs fail frequently before transactions time out. The timeout length is controlled by StarRocks FE configuration prepared_transaction_default_timeout_second whose default value is 86400 (1 day). You can set a smaller value to it to make transactions expired faster when the label prefix is not specified.
If you are certain that the Flink job will eventually recover from checkpoint or savepoint after a long downtime because of stop or continuous failover, please adjust the following StarRocks configurations accordingly, to avoid data loss.
- prepared_transaction_default_timeout_second: StarRocks FE configuration, default value is 86400. The value of this configuration needs to be larger than the downtime of the Flink job. Otherwise, the lingering transactions that are included in a successful checkpoint may be aborted because of timeout before you restart the Flink job, which leads to data loss.
  
  Note that when you set a larger value to this configuration, it is better to specify the value of sink.label-prefix so that the lingering transactions can be cleaned according to the label prefix and some information in checkpoint, instead of due to timeout (which may cause data loss).
- label_keep_max_second and label_keep_max_num: StarRocks FE configurations, default values are 259200 and 1000 respectively. For details, see FE configurations. The value of label_keep_max_second needs to be larger than the downtime of the Flink job. Otherwise, the Flink connector can not check the state of transactions in StarRocks by using the transaction labels saved in the Flink's savepoint or checkpoint and figure out whether these transactions are committed or not, which may eventually lead to data loss.
These configurations are mutable and can be modified by using ADMIN SET FRONTEND CONFIG:
```
  ADMIN SET FRONTEND CONFIG ("prepared_transaction_default_timeout_second" = "3600");
  ADMIN SET FRONTEND CONFIG ("label_keep_max_second" = "259200");
  ADMIN SET FRONTEND CONFIG ("label_keep_max_num" = "1000");
```

Flush Policy

The Flink connector will buffer the data in memory, and flush them in batch to StarRocks via Stream Load. How the flush is triggered is different between at-least-once and exactly-once.

For at-least-once, the flush will be triggered when any of the following conditions are met:

the bytes of buffered rows reaches the limit sink.buffer-flush.max-bytes
the number of buffered rows reaches the limit sink.buffer-flush.max-rows. (Only valid for sink version V1)
the elapsed time since the last flush reaches the limit sink.buffer-flush.interval-ms
a checkpoint is triggered

For exactly-once, the flush only happens when a checkpoint is triggered.

Monitoring load metrics

The Flink connector provides the following metrics to monitor loading.

Metric	Type	Description
totalFlushBytes	counter	successfully flushed bytes.
totalFlushRows	counter	number of rows successfully flushed.
totalFlushSucceededTimes	counter	number of times that the data is successfully flushed.
totalFlushFailedTimes	counter	number of times that the data fails to be flushed.
totalFilteredRows	counter	number of rows filtered, which is also included in totalFlushRows.

Examples

The following examples show how to use the Flink connector to load data into a StarRocks table with Flink SQL or Flink DataStream.

Preparations

Create a StarRocks table

Create a database test and create a Primary Key table score_board.

CREATE DATABASE `test`;

CREATE TABLE `test`.`score_board`
(
    `id` int(11) NOT NULL COMMENT "",
    `name` varchar(65533) NULL DEFAULT "" COMMENT "",
    `score` int(11) NOT NULL DEFAULT "0" COMMENT ""
)
ENGINE=OLAP
PRIMARY KEY(`id`)
COMMENT "OLAP"
DISTRIBUTED BY HASH(`id`);

Set up Flink environment

Download Flink binary Flink 1.15.2, and unzip it to directory flink-1.15.2.
Download Flink connector 1.2.7, and put it into the directory flink-1.15.2/lib.
Run the following commands to start a Flink cluster:
```
cd flink-1.15.2
./bin/start-cluster.sh
```

Network configuration

Ensure that the machine where Flink is located can access the FE nodes of the StarRocks cluster via the http_port (default: 8030) and query_port (default: 9030), and the BE nodes via the be_http_port (default: 8040).

Run with Flink SQL

Run the following command to start a Flink SQL client.
```
./bin/sql-client.sh
```

Create a Flink table score_board, and insert values into the table via Flink SQL Client. Note you must define the primary key in the Flink DDL if you want to load data into a Primary Key table of StarRocks. It's optional for other types of StarRocks tables.

CREATE TABLE `score_board` (
    `id` INT,
    `name` STRING,
    `score` INT,
    PRIMARY KEY (id) NOT ENFORCED
) WITH (
    'connector' = 'starrocks',
    'jdbc-url' = 'jdbc:mysql://127.0.0.1:9030',
    'load-url' = '127.0.0.1:8030',
    'database-name' = 'test',
    
    'table-name' = 'score_board',
    'username' = 'root',
    'password' = ''
);

INSERT INTO `score_board` VALUES (1, 'starrocks', 100), (2, 'flink', 100);

Run with Flink DataStream

There are several ways to implement a Flink DataStream job according to the type of the input records, such as a CSV Java String, a JSON Java String or a custom Java object.

The input records are CSV-format String. See LoadCsvRecords for a complete example.

/**
 * Generate CSV-format records. Each record has three values separated by "\t". 
 * These values will be loaded to the columns `id`, `name`, and `score` in the StarRocks table.
 */
String[] records = new String[]{
        "1\tstarrocks-csv\t100",
        "2\tflink-csv\t100"
};
DataStream<String> source = env.fromElements(records);

/**
 * Configure the connector with the required properties.
 * You also need to add properties "sink.properties.format" and "sink.properties.column_separator"
 * to tell the connector the input records are CSV-format, and the column separator is "\t".
 * You can also use other column separators in the CSV-format records,
 * but remember to modify the "sink.properties.column_separator" correspondingly.
 */
StarRocksSinkOptions options = StarRocksSinkOptions.builder()
        .withProperty("jdbc-url", jdbcUrl)
        .withProperty("load-url", loadUrl)
        .withProperty("database-name", "test")
        .withProperty("table-name", "score_board")
        .withProperty("username", "root")
        .withProperty("password", "")
        .withProperty("sink.properties.format", "csv")
        .withProperty("sink.properties.column_separator", "\t")
        .build();
// Create the sink with the options.
SinkFunction<String> starRockSink = StarRocksSink.sink(options);
source.addSink(starRockSink);

The input records are JSON-format String. See LoadJsonRecords for a complete example.

/**
 * Generate JSON-format records. 
 * Each record has three key-value pairs corresponding to the columns `id`, `name`, and `score` in the StarRocks table.
 */
String[] records = new String[]{
        "{\"id\":1, \"name\":\"starrocks-json\", \"score\":100}",
        "{\"id\":2, \"name\":\"flink-json\", \"score\":100}",
};
DataStream<String> source = env.fromElements(records);

/** 
 * Configure the connector with the required properties.
 * You also need to add properties "sink.properties.format" and "sink.properties.strip_outer_array"
 * to tell the connector the input records are JSON-format and to strip the outermost array structure. 
 */
StarRocksSinkOptions options = StarRocksSinkOptions.builder()
        .withProperty("jdbc-url", jdbcUrl)
        .withProperty("load-url", loadUrl)
        .withProperty("database-name", "test")
        .withProperty("table-name", "score_board")
        .withProperty("username", "root")
        .withProperty("password", "")
        .withProperty("sink.properties.format", "json")
        .withProperty("sink.properties.strip_outer_array", "true")
        .build();
// Create the sink with the options.
SinkFunction<String> starRockSink = StarRocksSink.sink(options);
source.addSink(starRockSink);

The input records are custom Java objects. See LoadCustomJavaRecords for a complete example.

In this example, the input record is a simple POJO RowData.

public static class RowData {
        public int id;
        public String name;
        public int score;

        public RowData() {}

        public RowData(int id, String name, int score) {
            this.id = id;
            this.name = name;
            this.score = score;
        }
    }

The main program is as follows:

// Generate records which use RowData as the container.
RowData[] records = new RowData[]{
        new RowData(1, "starrocks-rowdata", 100),
        new RowData(2, "flink-rowdata", 100),
    };
DataStream<RowData> source = env.fromElements(records);

// Configure the connector with the required properties.
StarRocksSinkOptions options = StarRocksSinkOptions.builder()
        .withProperty("jdbc-url", jdbcUrl)
        .withProperty("load-url", loadUrl)
        .withProperty("database-name", "test")
        .withProperty("table-name", "score_board")
        .withProperty("username", "root")
        .withProperty("password", "")
        .build();

/**
 * The Flink connector will use a Java object array (Object[]) to represent a row to be loaded into the StarRocks table,
 * and each element is the value for a column.
 * You need to define the schema of the Object[] which matches that of the StarRocks table.
 */
TableSchema schema = TableSchema.builder()
        .field("id", DataTypes.INT().notNull())
        .field("name", DataTypes.STRING())
        .field("score", DataTypes.INT())
        // When the StarRocks table is a Primary Key table, you must specify notNull(), for example, DataTypes.INT().notNull(), for the primary key `id`.
        .primaryKey("id")
        .build();
// Transform the RowData to the Object[] according to the schema.
RowDataTransformer transformer = new RowDataTransformer();
// Create the sink with the schema, options, and transformer.
SinkFunction<RowData> starRockSink = StarRocksSink.sink(schema, options, transformer);
source.addSink(starRockSink);

The RowDataTransformer in the main program is defined as follows:

private static class RowDataTransformer implements StarRocksSinkRowBuilder<RowData> {

    /**
     * Set each element of the object array according to the input RowData.
     * The schema of the array matches that of the StarRocks table.
     */
    @Override
    public void accept(Object[] internalRow, RowData rowData) {
        internalRow[0] = rowData.id;
        internalRow[1] = rowData.name;
        internalRow[2] = rowData.score;
        // When the StarRocks table is a Primary Key table, you need to set the last element to indicate whether the data loading is an UPSERT or DELETE operation.
        internalRow[internalRow.length - 1] = StarRocksSinkOP.UPSERT.ordinal();
    }
}  

Synchronize data with Flink CDC 3.0 (with schema change supported)

Flink CDC 3.0 framework can be used to easily build a streaming ELT pipeline from CDC sources (such as MySQL and Kafka) to StarRocks. The pipeline can synchronize whole database, merged sharding tables, and schema changes from sources to StarRocks.

Since v1.2.9, the Flink connector for StarRocks is integrated into this framework as StarRocks Pipeline Connector. The StarRocks Pipeline Connector supports:

Automatic creation of databases and tables
Schema change synchronization
Full and incremental data synchronization

For quick start, see Streaming ELT from MySQL to StarRocks using Flink CDC 3.0 with StarRocks Pipeline Connector.

Best practices

Load data to a Primary Key table

This section will show how to load data to a StarRocks Primary Key table to achieve partial updates and conditional updates. You can see Change data through loading for the introduction of those features. These examples use Flink SQL.

Preparations

Create a database test and create a Primary Key table score_board in StarRocks.

CREATE DATABASE `test`;

CREATE TABLE `test`.`score_board`
(
    `id` int(11) NOT NULL COMMENT "",
    `name` varchar(65533) NULL DEFAULT "" COMMENT "",
    `score` int(11) NOT NULL DEFAULT "0" COMMENT ""
)
ENGINE=OLAP
PRIMARY KEY(`id`)
COMMENT "OLAP"
DISTRIBUTED BY HASH(`id`);

Partial update

This example will show how to load data only to columns id and name.

Insert two data rows into the StarRocks table score_board in MySQL client.

mysql> INSERT INTO `score_board` VALUES (1, 'starrocks', 100), (2, 'flink', 100);

mysql> select * from score_board;
+------+-----------+-------+
| id   | name      | score |
+------+-----------+-------+
|    1 | starrocks |   100 |
|    2 | flink     |   100 |
+------+-----------+-------+
2 rows in set (0.02 sec)

Create a Flink table score_board in Flink SQL client.
- Define the DDL which only includes the columns id and name.
- Set the option sink.properties.partial_update to true which tells the Flink connector to perform partial updates.
- If the Flink connector version <= 1.2.7, you also need to set the option sink.properties.columns to id,name,__op to tells the Flink connector which columns need to be updated. Note that you need to append the field __op at the end. The field __op indicates that the data loading is an UPSERT or DELETE operation, and its values are set by the connector automatically.
```
CREATE TABLE `score_board` (
    `id` INT,
    `name` STRING,
    PRIMARY KEY (id) NOT ENFORCED
) WITH (
    'connector' = 'starrocks',
    'jdbc-url' = 'jdbc:mysql://127.0.0.1:9030',
    'load-url' = '127.0.0.1:8030',
    'database-name' = 'test',
    'table-name' = 'score_board',
    'username' = 'root',
    'password' = '',
    'sink.properties.partial_update' = 'true',
    -- only for Flink connector version <= 1.2.7
    'sink.properties.columns' = 'id,name,__op'
); 
```
Insert two data rows into the Flink table. The primary keys of the data rows are as same as these of rows in the StarRocks table. but the values in the column name are modified.
```
INSERT INTO `score_board` VALUES (1, 'starrocks-update'), (2, 'flink-update');
```

Query the StarRocks table in MySQL client.

mysql> select * from score_board;
+------+------------------+-------+
| id   | name             | score |
+------+------------------+-------+
|    1 | starrocks-update |   100 |
|    2 | flink-update     |   100 |
+------+------------------+-------+
2 rows in set (0.02 sec)

You can see that only values for name change, and the values for score do not change.

Conditional update

This example will show how to do conditional update according to the value of column score. The update for an id takes effect only when the new value for score is has a greater or equal to the old value.

Insert two data rows into the StarRocks table in MySQL client.

mysql> INSERT INTO `score_board` VALUES (1, 'starrocks', 100), (2, 'flink', 100);

mysql> select * from score_board;
+------+-----------+-------+
| id   | name      | score |
+------+-----------+-------+
|    1 | starrocks |   100 |
|    2 | flink     |   100 |
+------+-----------+-------+
2 rows in set (0.02 sec)

Create a Flink table score_board in the following ways:

Define the DDL including all of columns.
Set the option sink.properties.merge_condition to score to tell the connector to use the column score as the condition.
Set the option sink.version to V1 which tells the connector to use Stream Load.

CREATE TABLE `score_board` (
    `id` INT,
    `name` STRING,
    `score` INT,
    PRIMARY KEY (id) NOT ENFORCED
) WITH (
    'connector' = 'starrocks',
    'jdbc-url' = 'jdbc:mysql://127.0.0.1:9030',
    'load-url' = '127.0.0.1:8030',
    'database-name' = 'test',
    'table-name' = 'score_board',
    'username' = 'root',
    'password' = '',
    'sink.properties.merge_condition' = 'score',
    'sink.version' = 'V1'
    );

Insert two data rows into the Flink table. The primary keys of the data rows are as same as these of rows in the StarRocks table. The first data row has a smaller value in the column score, and the second data row has a larger value in the column score.
```
INSERT INTO `score_board` VALUES (1, 'starrocks-update', 99), (2, 'flink-update', 101);
```

Query the StarRocks table in MySQL client.

mysql> select * from score_board;
+------+--------------+-------+
| id   | name         | score |
+------+--------------+-------+
|    1 | starrocks    |   100 |
|    2 | flink-update |   101 |
+------+--------------+-------+
2 rows in set (0.03 sec)

You can see that only the values of the second data row change, and the values of the first data row do not change.

Load data into columns of BITMAP type

BITMAP is often used to accelerate count distinct, such as counting UV, see Use Bitmap for exact Count Distinct. Here we take the counting of UV as an example to show how to load data into columns of the BITMAP type.

Create a StarRocks Aggregate table in MySQL client.

In the database test, create an Aggregate table page_uv where the column visit_users is defined as the BITMAP type and configured with the aggregate function BITMAP_UNION.

CREATE TABLE `test`.`page_uv` (
  `page_id` INT NOT NULL COMMENT 'page ID',
  `visit_date` datetime NOT NULL COMMENT 'access time',
  `visit_users` BITMAP BITMAP_UNION NOT NULL COMMENT 'user ID'
) ENGINE=OLAP
AGGREGATE KEY(`page_id`, `visit_date`)
DISTRIBUTED BY HASH(`page_id`);

Create a Flink table in Flink SQL client.

The column visit_user_id in the Flink table is of BIGINT type, and we want to load this column to the column visit_users of BITMAP type in the StarRocks table. So when defining the DDL of the Flink table, note that:
- Because Flink does not support BITMAP, you need to define a column visit_user_id as BIGINT type to represent the column visit_users of BITMAP type in the StarRocks table.
- You need to set the option sink.properties.columns to page_id,visit_date,user_id,visit_users=to_bitmap(visit_user_id), which tells the connector the column mapping between the Flink table and StarRocks table. Also you need to use to_bitmap function to tell the connector to convert the data of BIGINT type into BITMAP type.
```
CREATE TABLE `page_uv` (
    `page_id` INT,
    `visit_date` TIMESTAMP,
    `visit_user_id` BIGINT
) WITH (
    'connector' = 'starrocks',
    'jdbc-url' = 'jdbc:mysql://127.0.0.1:9030',
    'load-url' = '127.0.0.1:8030',
    'database-name' = 'test',
    'table-name' = 'page_uv',
    'username' = 'root',
    'password' = '',
    'sink.properties.columns' = 'page_id,visit_date,visit_user_id,visit_users=to_bitmap(visit_user_id)'
);
```

Load data into Flink table in Flink SQL client.

INSERT INTO `page_uv` VALUES
   (1, CAST('2020-06-23 01:30:30' AS TIMESTAMP), 13),
   (1, CAST('2020-06-23 01:30:30' AS TIMESTAMP), 23),
   (1, CAST('2020-06-23 01:30:30' AS TIMESTAMP), 33),
   (1, CAST('2020-06-23 02:30:30' AS TIMESTAMP), 13),
   (2, CAST('2020-06-23 01:30:30' AS TIMESTAMP), 23);

Calculate page UVs from the StarRocks table in MySQL client.

MySQL [test]> SELECT `page_id`, COUNT(DISTINCT `visit_users`) FROM `page_uv` GROUP BY `page_id`;
+---------+-----------------------------+
| page_id | count(DISTINCT visit_users) |
+---------+-----------------------------+
|       2 |                           1 |
|       1 |                           3 |
+---------+-----------------------------+
2 rows in set (0.05 sec)

Load data into columns of HLL type

HLL can be used for approximate count distinct, see Use HLL for approximate count distinct.

Here we take the counting of UV as an example to show how to load data into columns of the HLL type.

Create a StarRocks Aggregate table

In the database test, create an Aggregate table hll_uv where the column visit_users is defined as the HLL type and configured with the aggregate function HLL_UNION.

CREATE TABLE `hll_uv` (
  `page_id` INT NOT NULL COMMENT 'page ID',
  `visit_date` datetime NOT NULL COMMENT 'access time',
  `visit_users` HLL HLL_UNION NOT NULL COMMENT 'user ID'
) ENGINE=OLAP
AGGREGATE KEY(`page_id`, `visit_date`)
DISTRIBUTED BY HASH(`page_id`);

Create a Flink table in Flink SQL client.

The column visit_user_id in the Flink table is of BIGINT type, and we want to load this column to the column visit_users of HLL type in the StarRocks table. So when defining the DDL of the Flink table, note that:
- Because Flink does not support BITMAP, you need to define a column visit_user_id as BIGINT type to represent the column visit_users of HLL type in the StarRocks table.
- You need to set the option sink.properties.columns to page_id,visit_date,user_id,visit_users=hll_hash(visit_user_id) which tells the connector the column mapping between Flink table and StarRocks table. Also you need to use hll_hash function to tell the connector to convert the data of BIGINT type into HLL type.
```
CREATE TABLE `hll_uv` (
    `page_id` INT,
    `visit_date` TIMESTAMP,
    `visit_user_id` BIGINT
) WITH (
    'connector' = 'starrocks',
    'jdbc-url' = 'jdbc:mysql://127.0.0.1:9030',
    'load-url' = '127.0.0.1:8030',
    'database-name' = 'test',
    'table-name' = 'hll_uv',
    'username' = 'root',
    'password' = '',
    'sink.properties.columns' = 'page_id,visit_date,visit_user_id,visit_users=hll_hash(visit_user_id)'
);
```

Load data into Flink table in Flink SQL client.

INSERT INTO `hll_uv` VALUES
   (3, CAST('2023-07-24 12:00:00' AS TIMESTAMP), 78),
   (4, CAST('2023-07-24 13:20:10' AS TIMESTAMP), 2),
   (3, CAST('2023-07-24 12:30:00' AS TIMESTAMP), 674);

Calculate page UVs from the StarRocks table in MySQL client.

mysql> SELECT `page_id`, COUNT(DISTINCT `visit_users`) FROM `hll_uv` GROUP BY `page_id`;
**+---------+-----------------------------+
| page_id | count(DISTINCT visit_users) |
+---------+-----------------------------+
|       3 |                           2 |
|       4 |                           1 |
+---------+-----------------------------+
2 rows in set (0.04 sec)

Continuously load data from Apache Flink®

Version requirements​

Obtain Flink connector​

Download the compiled Jar file​

Maven Dependency​

Compile by yourself​

Options​

Data type mapping between Flink and StarRocks​

Usage notes​

Exactly Once​

Flush Policy​

Monitoring load metrics​

Examples​

Preparations​

Create a StarRocks table​

Set up Flink environment​

Network configuration​

Run with Flink SQL​

Run with Flink DataStream​

Synchronize data with Flink CDC 3.0 (with schema change supported)​

Best practices​

Load data to a Primary Key table​

Preparations​

Partial update​

Conditional update​

Load data into columns of BITMAP type​

Load data into columns of HLL type​

What did you think of this doc?

Version requirements

Obtain Flink connector

Download the compiled Jar file

Maven Dependency

Compile by yourself

Options

Data type mapping between Flink and StarRocks

Usage notes

Exactly Once

Flush Policy

Monitoring load metrics

Examples

Preparations

Create a StarRocks table

Set up Flink environment

Network configuration

Run with Flink SQL

Run with Flink DataStream

Synchronize data with Flink CDC 3.0 (with schema change supported)

Best practices

Load data to a Primary Key table

Preparations

Partial update

Conditional update

Load data into columns of BITMAP type

Load data into columns of HLL type