Import Overview

Supported data sources

Doris provides a variety of data import solutions, and you can choose different data import methods for different data sources.

By scene

Data SourceImport Method
Object Storage (s3), HDFSImport data using Broker
Local fileImport local data
KafkaSubscribe to Kafka data
Mysql, PostgreSQL, Oracle, SQLServerSync data via external table
Import via JDBCSync data using JDBC
Import JSON format dataJSON format data import
MySQL BinlogBinlog Load

Divided by import method

Import method nameUse method
Spark LoadImport external data via Spark
Broker LoadImport external storage data via Broker
Stream LoadStream import data (local file and memory data)
Routine LoadImport Kafka data
Binlog Loadcollect Mysql Binlog import data
Insert IntoExternal table imports data through INSERT
S3 LoadObject storage data import of S3 protocol

Supported data formats

Different import methods support slightly different data formats.

Import MethodsSupported Formats
Broker Loadparquet, orc, csv, gzip
Stream Loadcsv, json, parquet, orc
Routine Loadcsv, json

import instructions

The data import implementation of Apache Doris has the following common features, which are introduced here to help you better use the data import function

Import atomicity guarantees

Each import job of Doris, whether it is batch import using Broker Load or single import using INSERT statement, is a complete transaction operation. The import transaction can ensure that the data in a batch takes effect atomically, and there will be no partial data writing.

At the same time, an import job will have a Label. This Label is unique under a database (Database) and is used to uniquely identify an import job. Label can be specified by the user, and some import functions will also be automatically generated by the system.

Label is used to ensure that the corresponding import job can only be successfully imported once. A successfully imported Label, when used again, will be rejected with the error Label already used. Through this mechanism, At-Most-Once semantics can be implemented in Doris. If combined with the At-Least-Once semantics of the upstream system, the Exactly-Once semantics of imported data can be achieved.

For best practices on atomicity guarantees, see Importing Transactions and Atomicity.

Synchronous and asynchronous imports

Import methods are divided into synchronous and asynchronous. For the synchronous import method, the returned result indicates whether the import succeeds or fails. For the asynchronous import method, a successful return only means that the job was submitted successfully, not that the data was imported successfully. You need to use the corresponding command to check the running status of the import job.

Import the data of array type

The array function can only be supported in vectorization scenarios, but non-vectorization scenarios are not supported. if you want to apply the array function to import data, you should enable vectorization engine. Then you need to cast the input parameter column into the array type according to the parameter of the array function. Finally, you can continue to use the array function.

For example, in the following import, you need to cast columns b14 and a13 into array<string> type, and then use the array_union function.

  1. LOAD LABEL label_03_14_49_34_898986_19090452100 (
  2. DATA INFILE("hdfs://test.hdfs.com:9000/user/test/data/sys/load/array_test.data")
  3. INTO TABLE `test_array_table`
  4. COLUMNS TERMINATED BY "|" (`k1`, `a1`, `a2`, `a3`, `a4`, `a5`, `a6`, `a7`, `a8`, `a9`, `a10`, `a11`, `a12`, `a13`, `b14`)
  5. SET(a14=array_union(cast(b14 as array<string>), cast(a13 as array<string>))) WHERE size(a2) > 270)
  6. WITH BROKER "hdfs" ("username"="test_array", "password"="")
  7. PROPERTIES( "max_filter_ratio"="0.8" );