Auto create metadata

Automatically creating schema means creating time series based on the characteristics of written data in case time series haven't defined by users themselves. This function can not only solve the problem that entities and measurements are difficult to predict and model in advance under massive time series scenarios, but also provide users with an out-of-the-box writing experience.

Auto create database metadata

• enable_auto_create_schema

Name	enable_auto_create_schema
Description	whether creating schema automatically is enabled
Type	boolean
Default	true
Effective	After restarting system

• default_storage_group_level

Name	default_storage_group_level
Description	Specify which level database is in the time series, the default level is 1 (root is on level 0)
Type	int
Default	1
Effective	Only allowed to be modified in first start up

Illustrated as the following figure:

• When default_storage_group_level=1, root.turbine1 and root.turbine2 will be created as database.

• When default_storage_group_level=2, root.turbine1.d1, root.turbine1.d2, root.turbine2.d1 and root.turbine2.d2 will be created as database.

Auto create time series metadata(specify data type in the frontend)

• Users should specify data type when writing:

  • insertTablet method in Session module.
  • insert method using TSDataType in Session module.

    public void insertRecord(String deviceId, long time, List<String> measurements, List<TSDataType> types, Object... values);
    public void insertRecords(List<String> deviceIds, List<Long> times, List<List<String>> measurementsList, List<List<TSDataType>> typesList, List<List<Object>> valuesList);
    

  • ......

• Efficient, time series are auto created when inserting data.

Auto create time series metadata(infer data type in the backend)

• Just pass string, and the database will infer the data type:

  • insert command in CLI module.
  • insert method without using TSDataType in Session module.

    public void insertRecord(String deviceId, long time, List<String> measurements, List<TSDataType> types, List<Object> values);
    public void insertRecords(List<String> deviceIds, List<Long> times, List<List<String>> measurementsList, List<List<String>> valuesList);
    

  • ......

• Since type inference will increase the writing time, the efficiency of auto creating time series metadata through type inference is lower than that of auto creating time series metadata through specifying data type. We recommend users choose specifying data type in the frontend when possible.

Type inference

Data(String Format)	Format Type	iotdb-datanode.properties	Default
true	boolean	boolean_string_infer_type	BOOLEAN
1	integer	integer_string_infer_type	FLOAT
17000000（integer > 2^24）	integer	long_string_infer_type	DOUBLE
1.2	floating	floating_string_infer_type	FLOAT
NaN	nan	nan_string_infer_type	DOUBLE
‘I am text’	text	x	x

• Data types can be configured as BOOLEAN, INT32, INT64, FLOAT, DOUBLE, TEXT.

• long_string_infer_type is used to avoid precision loss caused by using integer_string_infer_type=FLOAT to infer num > 2^24.

Encoding Type

Data Type	iotdb-datanode.properties	Default
BOOLEAN	default_boolean_encoding	RLE
INT32	default_int32_encoding	RLE
INT64	default_int64_encoding	RLE
FLOAT	default_float_encoding	GORILLA
DOUBLE	default_double_encoding	GORILLA
TEXT	default_text_encoding	PLAIN

• Encoding types can be configured as PLAIN, RLE, TS_2DIFF, GORILLA, DICTIONARY.

• The corresponding relationship between data types and encoding types is detailed in Encoding.