Apache Flink
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 on the target StarRocks table. 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 useflink-connector-starrocks-1.2.7_flink-1.15.jar
. -
Prior to Flink 1.15, it's
flink-connector-starrocks-${connector_version}_flink-${flink_version}_${scala_version}.jar
. For example, if you install Flink 1.14 and Scala 2.12 in your environment, and you want to use Flink connector 1.2.7, you can useflink-connector-starrocks-1.2.7_flink-1.14_2.12.jar
.
NOTICE
In general, the latest version of the Flink connector only maintains compatibility with the three most recent versions of Flink.
Download the compiled Jar file
Directly download the corresponding version of the Flink connector Jar file from the Maven Central Repository.
Maven Dependency
In your Maven project's pom.xml
file, add the Flink connector as a dependency according to the following format. Replace flink_version
, scala_version
, and connector_version
with the respective versions.
-
In Flink 1.15 and later
<dependency>
<groupId>com.starrocks</groupId>
<artifactId>flink-connector-starrocks</artifactId>
<version>${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 source code.
-
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 asflink-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
connector
Required: Yes
Default value: NONE
Description: The connector that you want to use. The value must be "starrocks".
jdbc-url
Required: Yes
Default value: NONE
Description: The address that is used to connect to the MySQL server of the FE. You can specify multiple addresses, 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
Required: Yes
Default value: NONE
Description: The address that is used to connect to the HTTP server of the FE. You can specify multiple addresses, which must be separated by a semicolon (;). Format: <fe_host1>:<fe_http_port1>;<fe_host2>:<fe_http_port2>
.
database-name
Required: Yes
Default value: NONE
Description: The name of the StarRocks database into which you want to load data.
table-name
Required: Yes
Default value: NONE
Description: The name of the table that you want to use to load data into StarRocks.
username
Required: Yes
Default value: NONE
Description: The username of the account that you want to use to load data into StarRocks. The account needs SELECT and INSERT privileges on the target StarRocks table.
password
Required: Yes
Default value: NONE
Description: The password of the preceding account.
sink.version
Required: No
Default value: AUTO
Description: 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. RecommendsV2
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 chooseV2
automatically, otherwise chooseV1
sink.label-prefix
Required: No
Default value: NONE
Description: 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.semantic
Required: No
Default value: at-least-once
Description: The semantic guaranteed by sink. Valid values: at-least-once and exactly-once.
sink.buffer-flush.max-bytes
Required: No
Default value: 94371840(90M)
Description: 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. This parameter only takes effect when sink.semantic
is set to at-least-once
. If sink.semantic
is set to exactly-once
, the data in memory is flushed when a Flink checkpoint is triggered. In this circumstance, this parameter does not take effect.
sink.buffer-flush.max-rows
Required: No
Default value: 500000
Description: 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 sink.version
is V1
and sink.semantic
is at-least-once
. Valid values: 64000 to 5000000.
sink.buffer-flush.interval-ms
Required: No
Default value: 300000
Description: The interval at which data is flushed. This parameter is available only when sink.semantic
is at-least-once
. Valid values: 1000 to 3600000. Unit: ms.
sink.max-retries
Required: No
Default value: 3
Description: 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
Required: No
Default value: 30000
Description: 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
Required: No
Default value: -1
Description: 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
Required: No
Default value: 10000
Description: 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
Required: No
Default value: true
Description: 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.parallelism
Required: No
Default value: NONE
Description: The parallelism of loading. Only available for Flink SQL. If this parameter is not specified, Flink planner decides the parallelism. In the scenario of multi-parallelism, users need to guarantee data is written in the correct order.
sink.properties.*
Required: No
Default value: NONE
Description: 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
Required: No
Default value: csv
Description: 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.column_separator
Required: No
Default value: \t
Description: The column separator for CSV-formatted data.
sink.properties.row_delimiter
Required: No
Default value: \n
Description: The row delimiter for CSV-formatted data.
sink.properties.max_filter_ratio
Required: No
Default value: 0
Description: 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.properties.partial_update
Required: NO
Default value: FALSE
Description: Whether to use partial updates. Valid values: TRUE
and FALSE
. Default value: FALSE
, indicating to disable this feature.
sink.properties.partial_update_mode
Required: NO
Default value: row
Description: Specifies the mode for partial updates. Valid values: row
and column
.
- The value
row
(default) means partial updates in row mode, which is more suitable for real-time updates with many columns and small batches. - The value
column
means partial updates in column mode, which is more suitable for batch updates with few columns and many rows. In such scenarios, enabling the column mode offers faster update speeds. For example, in a table with 100 columns, if only 10 columns (10% of the total) are updated for all rows, the update speed of the column mode is 10 times faster.
sink.properties.strict_mode
Required: No
Default value: false
Description: 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
Required: No
Default value: NONE
Description: 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 beexactly-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 useSHOW 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 configurationprepared_transaction_default_timeout_second
whose default value is86400
(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 is86400
. 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
andlabel_keep_max_num
: StarRocks FE configurations, default values are259200
and1000
respectively. For details, see FE configurations. The value oflabel_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.
Name | Type | Description |
---|---|---|
totalFlushBytes | counter | successfully flushed bytes. |
totalFlushRows | counter | number of rows successfully flushed. |
totalFlushSucceededTimes | counter | number of times that the data-batch been successfully flushed. |
totalFlushFailedTimes | counter | number of times that the flushing been failed. |
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.
It is advised to use StarRocks v3.2.1 and later versions to enable fast_schema_evolution. It will improve the speed of adding or dropping columns and reduce resource usage.
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
andname
. - Set the option
sink.properties.partial_update
totrue
which tells the Flink connector to perform partial updates. - If the Flink connector version
<=
1.2.7, you also need to set the optionsink.properties.columns
toid,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'
); - Define the DDL which only includes the columns
-
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 forscore
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
toscore
to tell the connector to use the columnscore
as the condition. - Set the option
sink.version
toV1
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 columnscore
.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 tablepage_uv
where the columnvisit_users
is defined as theBITMAP
type and configured with the aggregate functionBITMAP_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 ofBIGINT
type, and we want to load this column to the columnvisit_users
ofBITMAP
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 columnvisit_user_id
asBIGINT
type to represent the columnvisit_users
ofBITMAP
type in the StarRocks table. - You need to set the option
sink.properties.columns
topage_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 useto_bitmap
function to tell the connector to convert the data ofBIGINT
type intoBITMAP
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)'
); - Because Flink does not support
-
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 tablehll_uv
where the columnvisit_users
is defined as theHLL
type and configured with the aggregate functionHLL_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 ofBIGINT
type, and we want to load this column to the columnvisit_users
ofHLL
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 columnvisit_user_id
asBIGINT
type to represent the columnvisit_users
ofHLL
type in the StarRocks table. - You need to set the option
sink.properties.columns
topage_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 usehll_hash
function to tell the connector to convert the data ofBIGINT
type intoHLL
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)'
); - Because Flink does not support
-
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)