Introduction

You can import semi-structured data (e.g. JSON) by stream load or routine load.

Usage Scenarios

  • Stream Load: For JSON data stored in text files, use stream load to import.
  • Routine Load: For JSON data in Kafka, use routine load to import.

Stream Load Import

Sample data:

{ "id": 123, "city" : "beijing"},
{ "id": 456, "city" : "shanghai"},
    ...

Example:

curl -v --location-trusted -u root: \
    -H "format: json" -H "jsonpaths: [\"$.id\", \"$.city\"]" \
    -T example.json \
    http://FE_HOST:HTTP_PORT/api/DATABASE/TABLE/_stream_load

The format: json parameter allows you to execute the format of the imported data. jsonpaths is used to execute the corresponding data import path.

Related parameters:

  • jsonpaths: Select the JSON path for each column
  • json_root: Select the column where the JSON starts to be parsed
  • strip_outer_array: Crop the outermost array field
  • strict_mode: Strictly filter for column type conversion during import

When the JSON data schema and StarRocks data schema are not exactly the same, modify the Jsonpath.

Sample data:

{"k1": 1, "k2": 2}

Import example:

curl -v --location-trusted -u root: \
    -H "format: json" -H "jsonpaths: [\"$.k2\", \"$.k1\"]" \
    -H "columns: k2, tmp_k1, k1 = tmp_k1 * 100" \
    -T example.json \
    http://127.0.0.1:8030/api/db1/tbl1/_stream_load

The ETL operation of multiplying k1 by 100 is performed during the import, and the column is matched with the original data by Jsonpath.

The import results are as follows:

+------+------+
| k1   | k2   |
+------+------+
|  100 |    2 |
+------+------+

For missing columns, if the column definition is nullable, then NULL will be added, or the default value can be added by ifnull.

Sample data:

[
    {"k1": 1, "k2": "a"},
    {"k1": 2},
    {"k1": 3, "k2": "c"},
]

Import Example-1:

curl -v --location-trusted -u root: \
    -H "format: json" -H "strip_outer_array: true" \
    -T example.json \
    http://127.0.0.1:8030/api/db1/tbl1/_stream_load

The import results are as follows:

+------+------+
| k1   | k2   |
+------+------+
|    1 | a    |
+------+------+
|    2 | NULL |
+------+------+
|    3 | c    |
+------+------+

Import Example-2:

curl -v --location-trusted -u root: \
    -H "format: json" -H "strip_outer_array: true" \
    -H "jsonpaths: [\"$.k1\", \"$.k2\"]" \
    -H "columns: k1, tmp_k2, k2 = ifnull(tmp_k2, 'x')" \
    -T example.json \
    http://127.0.0.1:8030/api/db1/tbl1/_stream_load

The import results are as follows:

+------+------+
| k1   | k2   |
+------+------+
|    1 | a    |
+------+------+
|    2 | x    |
+------+------+
|    3 | c    |
+------+------+

Routine Load Import

Similar to stream load, the message content of Kafka data sources is treated as a complete JSON data.

  1. If a message contains multiple rows of data in array format, all rows will be imported and Kafka's offset will only be incremented by 1.
  2. If a JSON in Array format represents multiple rows of data, but the parsing of the JSON fails due to a JSON format error, the error row will only be incremented by 1 (given that the parsing fails, StarRocks cannot actually determine how many rows of data it contains, and can only record the error data as one row).

Use Canal to import StarRocks from MySQL with incremental sync binlogs

Canal is an open-source MySQL binlog synchronization tool from Alibaba, through which we can synchronize MySQL data to Kafka. The data is generated in JSON format in Kafka. Here is a demonstration of how to use routine load to synchronize data in Kafka for incremental data synchronization with MySQL.

  • In MySQL we have a data table with the following table creation statement.
CREATE TABLE `query_record` (
  `query_id` varchar(64) NOT NULL,
  `conn_id` int(11) DEFAULT NULL,
  `fe_host` varchar(32) DEFAULT NULL,
  `user` varchar(32) DEFAULT NULL,
  `start_time` datetime NOT NULL,
  `end_time` datetime DEFAULT NULL,
  `time_used` double DEFAULT NULL,
  `state` varchar(16) NOT NULL,
  `error_message` text,
  `sql` text NOT NULL,
  `database` varchar(128) NOT NULL,
  `profile` longtext,
  `plan` longtext,
  PRIMARY KEY (`query_id`),
  KEY `idx_start_time` (`start_time`) USING BTREE
) ENGINE=InnoDB DEFAULT CHARSET=utf8
  • Prerequisite: Make sure MySQL has binlog enabled and the format is ROW.
[mysqld]
log-bin=mysql-bin # Enable binlog
binlog-format=ROW # Select ROW mode
server_id=1 # MySQL replica     tion need to be defined, and do not duplicate canal's slaveId
  • Create an account and grant privileges to the MySQL slave:
CREATE USER canal IDENTIFIED BY 'canal';
GRANT SELECT, REPLICATION SLAVE, REPLICATION CLIENT ON *.* TO 'canal'@'%';
-- GRANT ALL PRIVILEGES ON *.* TO 'canal'@'%';
FLUSH PRIVILEGES;
  • Then download and install Canal.
wget https://github.com/alibaba/canal/releases/download/canal-1.0.17/canal.deployer-1.0.17.tar.gz

mkdir /tmp/canal
tar zxvf canal.deployer-$version.tar.gz -C /tmp/canal
  • Modify the configuration (MySQL related).

$ vi conf/example/instance.properties

## mysql serverId
canal.instance.mysql.slaveId = 1234
#position info,need to change to your own database information
canal.instance.master.address = 127.0.0.1:3306
canal.instance.master.journal.name =
canal.instance.master.position =
canal.instance.master.timestamp =
#canal.instance.standby.address =
#canal.instance.standby.journal.name =
#canal.instance.standby.position =
#canal.instance.standby.timestamp =
#username/password,need to change to your own database information
canal.instance.dbUsername = canal  
canal.instance.dbPassword = canal
canal.instance.defaultDatabaseName =
canal.instance.connectionCharset = UTF-8
#table regex
canal.instance.filter.regex = .\*\\\\..\*
# Select the name of the table to be synchronized and the partition name of the kafka target.
canal.mq.dynamicTopic=databasename.query_record
canal.mq.partitionHash= databasename.query_record:query_id
  • Modify the configuration (Kafka related).

$ vi /usr/local/canal/conf/canal.properties

# Available options: tcp(by default), kafka, RocketMQ
canal.serverMode = kafka
# ...
# kafka/rocketmq Cluster Configuration: 192.168.1.117:9092,192.168.1.118:9092,192.168.1.119:9092
canal.mq.servers = 127.0.0.1:6667
canal.mq.retries = 0
# This value can be increased in flagMessage mode, but do not exceed the maximum size of the MQ message.
canal.mq.batchSize = 16384
canal.mq.maxRequestSize = 1048576
# In flatMessage mode, please change this value to a larger value, 50-200 is recommended.
canal.mq.lingerMs = 1
canal.mq.bufferMemory = 33554432
# Canal's batch size with a default value of 50K. Please do not exceed 1M due to Kafka's maximum message size limit (under 900K)
canal.mq.canalBatchSize = 50
# Timeout of `Canal get`, in milliseconds. Empty indicates unlimited timeout.
canal.mq.canalGetTimeout = 100
# Whether the object is in flat json format
canal.mq.flatMessage = false
canal.mq.compressionType = none
canal.mq.acks = all
# Whether Kafka message delivery uses transactions
canal.mq.transaction = false
  • Initiation

bin/startup.sh

The corresponding synchronization log is shown in logs/example/example.log and in Kafka, with the following format:

{
    "data": [{
        "query_id": "3c7ebee321e94773-b4d79cc3f08ca2ac",
        "conn_id": "34434",
        "fe_host": "172.26.34.139",
        "user": "zhaoheng",
        "start_time": "2020-10-19 20:40:10.578",
        "end_time": "2020-10-19 20:40:10",
        "time_used": "1.0",
        "state": "FINISHED",
        "error_message": "",
        "sql": "COMMIT",
        "database": "",
        "profile": "",
        "plan": ""
    }, {
        "query_id": "7ff2df7551d64f8e-804004341bfa63ad",
        "conn_id": "34432",
        "fe_host": "172.26.34.139",
        "user": "zhaoheng",
        "start_time": "2020-10-19 20:40:10.566",
        "end_time": "2020-10-19 20:40:10",
        "time_used": "0.0",
        "state": "FINISHED",
        "error_message": "",
        "sql": "COMMIT",
        "database": "",
        "profile": "",
        "plan": ""
    }, {
        "query_id": "3a4b35d1c1914748-be385f5067759134",
        "conn_id": "34440",
        "fe_host": "172.26.34.139",
        "user": "zhaoheng",
        "start_time": "2020-10-19 20:40:10.601",
        "end_time": "1970-01-01 08:00:00",
        "time_used": "-1.0",
        "state": "RUNNING",
        "error_message": "",
        "sql": " SELECT SUM(length(lo_custkey)), SUM(length(c_custkey)) FROM lineorder_str INNER JOIN customer_str ON lo_custkey=c_custkey;",
        "database": "ssb",
        "profile": "",
        "plan": ""
    }],
    "database": "center_service_lihailei",
    "es": 1603111211000,
    "id": 122,
    "isDdl": false,
    "mysqlType": {
        "query_id": "varchar(64)",
        "conn_id": "int(11)",
        "fe_host": "varchar(32)",
        "user": "varchar(32)",
        "start_time": "datetime(3)",
        "end_time": "datetime",
        "time_used": "double",
        "state": "varchar(16)",
        "error_message": "text",
        "sql": "text",
        "database": "varchar(128)",
        "profile": "longtext",
        "plan": "longtext"
    },
    "old": null,
    "pkNames": ["query_id"],
    "sql": "",
    "sqlType": {
        "query_id": 12,
        "conn_id": 4,
        "fe_host": 12,
        "user": 12,
        "start_time": 93,
        "end_time": 93,
        "time_used": 8,
        "state": 12,
        "error_message": 2005,
        "sql": 2005,
        "database": 12,
        "profile": 2005,
        "plan": 2005
    },
    "table": "query_record",
    "ts": 1603111212015,
    "type": "INSERT"
}

Add json_root and strip_outer_array = true to import data from data.

create routine load manual.query_job on query_record   
columns (query_id,conn_id,fe_host,user,start_time,end_time,time_used,state,error_message,`sql`,`database`,profile,plan)  
PROPERTIES (  
    "format"="json",  
    "json_root"="$.data",
    "desired_concurrent_number"="1",  
    "strip_outer_array" ="true",    
    "max_error_number"="1000" 
) 
FROM KAFKA (     
    "kafka_broker_list"= "172.26.92.141:9092",     
    "kafka_topic" = "databasename.query_record" 
);

This completes the near real-time synchronization of data from MySQL to StarRocks.

View status and error messages of the import job by show routine load.