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## Encoding 

To improve the efficiency of data storage, it is necessary to encode data during data writing, thereby reducing the amount of disk space used. In the process of writing and reading data, the amount of data involved in the I/O operations can be reduced to improve performance. IoTDB supports the following encoding methods for different data types:

* PLAIN

PLAIN encoding, the default encoding mode, i.e, no encoding, supports multiple data types. It has high compression and decompression efficiency while suffering from low space storage efficiency.

* TS_2DIFF

Second-order differential encoding is more suitable for encoding monotonically increasing or decreasing sequence data, and is not recommended for sequence data with large fluctuations.

* RLE

Run-length encoding is suitable for storing sequence with continuous integer values, and is not recommended for sequence data with most of the time different values.

Run-length encoding can also be used to encode floating-point numbers, while it is necessary to specify reserved decimal digits (MAX\_POINT\_NUMBER) when creating time series. It is more suitable to store sequence data where floating-point values appear continuously, monotonously increasing or decreasing, and it is not suitable for storing sequence data with high precision requirements after the decimal point or with large fluctuations.

> TS_2DIFF and RLE have precision limit for data type of float and double. By default, two decimal places are reserved. GORILLA is recommended. 

* GORILLA

GORILLA encoding is lossless. It is more suitable for numerical sequence with similar values and is not recommended for sequence data with large fluctuations.

Currently, there are two versions of GORILLA encoding implementation, it is recommended to use `GORILLA` instead of `GORILLA_V1` (deprecated).

Usage restrictions: When using GORILLA to encode INT32 data, you need to ensure that there is no data point with the value `Integer.MIN_VALUE` in the sequence. When using GORILLA to encode INT64 data, you need to ensure that there is no data point with the value `Long.MIN_VALUE` in the sequence.

* REGULAR

Regular data encoding is more suitable for time encoding regular sequence increasing data (e.g. the timeseries with the same time elapsed between each data point), in which case it's better than TS_2DIFF.

Regular data encoding method is not suitable for the data with fluctuations (irregular data), and TS_2DIFF is recommended to deal with it.

* Correspondence between data type and encoding

The four encodings described in the previous sections are applicable to different data types. If the correspondence is wrong, the time series cannot be created correctly. The correspondence between the data type and its supported encodings is summarized in the Table below.

<center> **The correspondence between the data type and its supported encodings**

|Data Type	|Supported Encoding|
|:---:|:---:|
|BOOLEAN|	PLAIN, RLE|
|INT32	|PLAIN, RLE, TS_2DIFF, GORILLA|
|INT64	|PLAIN, RLE, TS_2DIFF, GORILLA|
|FLOAT	|PLAIN, RLE, TS_2DIFF, GORILLA|
|DOUBLE	|PLAIN, RLE, TS_2DIFF, GORILLA|
|TEXT	|PLAIN|

</center>