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 storage group 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 storage group 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 set as storage group.
  • When default_storage_group_level=2, root.turbine1.d1, root.turbine1.d2, root.turbine2.d1 and root.turbine2.d2 will be set as storage group.

auto create storage group example

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.

    1. public void insertRecord(String deviceId, long time, List<String> measurements, List<TSDataType> types, Object... values); 2. 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.

    1. public void insertRecord(String deviceId, long time, List<String> measurements, List<TSDataType> types, List<Object> values); 2. 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-engine.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-engine.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.